← Back to Part 1: Initial Analysis

Exploratory Data Analysis (EDA)

Our main goal is to evaluate and compare the effectiveness of different approaches to extracting features from images and different machine learning models for the classification task. The process will begin by converting the raw visual information into numerical representations using two selected methodologies: the Histogram of Oriented Gradients (HOG) method, representing classical approaches in feature engineering, and the deep learning-based Vision Transformer (ViT) model, representing modern approaches. Subsequently, Exploratory Data Analysis (EDA) will be performed on the extracted features. This stage will include the use of dimensionality reduction techniques (PCA, t-SNE) for visualizing and understanding the multidimensional structure of the data, as well as clustering algorithms (K-Means) to examine the existence of natural groups in the data. The purpose of EDA is to characterize the data, assess the quality of the features, and the degree of separability between the different classes. Finally, we will move on to the computational modeling phase, in which classified models will be developed and trained. We will start with a basic linear model (logistic regression) to serve as a benchmark, and move on to a more complex tree-based model (decision tree). The performance of the models will be quantitatively evaluated using standard metrics on a dedicated test set, and their results will be compared in order to draw conclusions about the optimal approach for this classification task in the context of the data and features examined.

# warnings.filterwarnings('ignore')

eda_df = df.copy()
# dummy_df = sample_n_per_label(df, n=100)

# Set random seed
SEED = 42
np.random.seed(SEED)
torch.manual_seed(SEED)
if torch.cuda.is_available():
    torch.cuda.manual_seed_all(SEED)

DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {DEVICE}")

# Define image size for consistency (adjust if needed)
IMG_WIDTH = 360
IMG_HEIGHT = 360
IMG_SIZE = (IMG_WIDTH, IMG_HEIGHT)

Using device: cpu

# Load Data
print(f"Dataset shape: {eda_df.shape}")
print("Value counts for labels:")
print(eda_df['label'].value_counts())

Dataset shape: (17092, 2)
Value counts for labels:
label
neutrophil      3329
eosinophil      3117
ig              2895
platelet        2348
erythroblast    1551
monocyte        1420
basophil        1218
lymphocyte      1214
Name: count, dtype: int64

Feature Extraction

Histogram of Oriented Gradients (HOG)

Our objective now is to extract classical shape-based features using HOG.

Histogram of Oriented Gradients (HOG) captures object shape information based on local gradient distributions. This step aims to convert each image into a fixed-length numerical vector representing these HOG features.

Things to note:
HOG works best on grayscale images, So the first step would be to convert an RGB image to grayscale.

def load_preprocess_image(img_path, target_size=(360, 360), color_mode='gray'):
    try:
        img = cv2.imread(img_path)
        if img is None:
            return None
        if color_mode == 'gray':
            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        elif color_mode == 'rgb':
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img = cv2.resize(img, target_size)
        return img
    except Exception as e:
        print(f"Error loading image {img_path}: {e}")
        return None

def extract_hog_features(image, orientations=9, pixels_per_cell=(8, 8), cells_per_block=(2, 2), block_norm='L2-Hys'):
    """
    Extract HOG features from a single image.

    Parameters:
    - image (numpy array): Input image (grayscale or RGB).
    - orientations (int): Number of orientation bins for HOG.
    - pixels_per_cell (tuple): Size (in pixels) of a cell.
    - cells_per_block (tuple): Number of cells in each block.
    - block_norm (str): Block normalization method ('L2-Hys', 'L1', etc.).

    Returns:
    - features (numpy array): HOG feature vector or None if processing fails.
    """
    try:
        if len(image.shape) == 3 and image.shape[2] == 3:
            image_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
        elif len(image.shape) == 2:
            image_gray = image
        else:
            raise ValueError("Unsupported image format for HOG")

        features = hog(
            image_gray,
            orientations=orientations,
            pixels_per_cell=pixels_per_cell,
            cells_per_block=cells_per_block,
            block_norm=block_norm,
            visualize=False
        )
        return features
    except Exception as e:
        print(f"Error extracting HOG features: {e}")
        return None

# Process HOG features for all images in a DataFrame
def process_hog_features(df, img_path_col, target_size=(360, 360), hog_params=None):
    if hog_params is None:
        hog_params = {
            'orientations': 9,
            'pixels_per_cell': (8, 8),
            'cells_per_block': (2, 2),
            'block_norm': 'L2-Hys'
        }

    hog_features_list = []
    print("Extracting HOG features...")
    for img_path in tqdm(df[img_path_col]):
        img = load_preprocess_image(img_path, target_size=target_size, color_mode='gray')
        if img is not None:
            features = extract_hog_features(img, **hog_params)
            hog_features_list.append(features)
        else:
            hog_features_list.append(None)

    # Filter out None values
    valid_indices = [i for i, f in enumerate(hog_features_list) if f is not None]
    df_filtered = df.iloc[valid_indices].copy()
    hog_features = np.array([hog_features_list[i] for i in valid_indices])

    print(f"HOG Features Extracted. Shape: {hog_features.shape}")
    return hog_features, df_filtered

Determining HOG Parameters

The HOG parameters (orientations, pixels_per_cell, cells_per_block, block_norm) significantly affect feature quality and computational efficiency. Here's how we determine their values:

orientations:
The number of orientation bins for gradient histograms. Our images have smooth, rounded structures. A value of 9 (dividing $0-180^{\circ}$ into 9 bins) is typically sufficient to capture these gradients without overfitting to noise. Increasing to 12 or 18 may add detail but increases feature dimensionality and computation time.
Default Value: 9 for balanced for capturing cell boundaries.

pixels_per_cell:
The size of each cell in pixels. Smaller cells capture finer details but increase feature vector size. For a 360x360 image, a cell size of 8x8 results in: Number of cells = (360 / 8) x (360 / 8) = 45 x 45 = 2025 cells.

Smaller cells (e.g., 4x4) yield 90x90 = 8100 cells, increasing detail but also computation and memory. Larger cells (e.g., 16x16) yield 22x22 = 484 cells, losing detail but faster.

Our images (blood cells) have distinct boundaries and internal structures. A cell size of 8x8 or 10x10 balances detail and efficiency. For 360x360 images, 10x10 (36x36 = 1296 cells) may be slightly faster while still capturing cell-level features. So we need to choose between (10, 10) for efficiency, or (8, 8) for more detail.

cells_per_block:
The number of cells in each block for normalization. Blocks slide across the image with a stride of one cell. With 2x2 cells per block and 8x8 pixels per cell, each block is 16x16 pixels. For a 360x360 image with 8x8 cells, the number of blocks is: (45 - 2 + 1) x (45 - 2 + 1) = 44 x 44 = 1936 blocks. Each block produces a feature vector of size orientations * cells_per_block[0] * cells_per_block[1] = 9 * 2 * 2 = 36. We see that 2x2 is standard and effective for capturing local context. Increasing to 3x3 (more context but larger features) is rarely needed unless cells have complex textures.
Default Value: (2, 2).

block_norm:
The normalization method for blocks to reduce lighting and contrast variations. Blood cell images often have varying staining intensities. 'L2-Hys' is commonly used in medical imaging for HOG.

Feature Vector Size Estimation
To confirm the output size of the HOG features for a 360x360 image:

• Cells: With pixels_per_cell=(8, 8), there are 45x45 = 2025 cells.
• Blocks: With cells_per_block=(2, 2), there are 44x44 = 1936 blocks (sliding window).
• Features per block: 9 orientations * 2x2 cells = 36 features.
• Total features: 1936 blocks * 36 features/block = 69,696 features per image.
• With (10, 10) pixels_per_cell:
  • Cells: 360 / 10 = 36x36 = 1296 cells.
  • Blocks: (36 - 2 + 1) x (36 - 2 + 1) = 35 x 35 = 1225 blocks.
  • Total features: 1225 * 36 = 44,100 features.

A smaller feature vector (e.g., with 10x10 cells) may be preferable for large datasets to reduce memory and computation while still capturing blood cell structures.

# Define HOG parameters
hog_params = {
    'orientations': 9,
    'pixels_per_cell': (10, 10),
    'cells_per_block': (2, 2),
    'block_norm': 'L2-Hys'
}

# Process HOG features
hog_features, df_filtered = process_hog_features(
    df=eda_df,
    img_path_col='imgPath',
    target_size=(360, 360),
    hog_params=hog_params
)

Extracting HOG features...

100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17092/17092 [03:17<00:00, 86.73it/s]

HOG Features Extracted. Shape: (17092, 44100)

Let's visualize the gradient patterns captured by the HOG algorithm for different images and labels.

def visualize_hog_samples(df, img_path_col, label_col, num_samples=3, target_size=(360, 360), hog_params=None):
    if hog_params is None:
        hog_params = {
            'orientations': 9,
            'pixels_per_cell': (8, 8),
            'cells_per_block': (2, 2),
            'block_norm': 'L2-Hys'
        }

    sample_indices = np.random.choice(len(df), min(num_samples, len(df)), replace=False)
    sample_paths = df[img_path_col].iloc[sample_indices]
    sample_labels = df[label_col].iloc[sample_indices]

    print("\nVisualizing HOG Features on Sample Images...")
    plt.figure(figsize=(12, 4 * len(sample_indices)))

    for i, (img_path, label) in enumerate(zip(sample_paths, sample_labels)):
        img_color = load_preprocess_image(img_path, target_size=target_size, color_mode='rgb')
        if img_color is None:
            print(f"Failed to load image: {img_path}")
            continue

        img_gray = cv2.cvtColor(img_color, cv2.COLOR_RGB2GRAY)

        try:
            _, hog_image = hog(
                img_gray,
                visualize=True,
                **hog_params
            )
        except Exception as e:
            print(f"Error computing HOG for {img_path}: {e}")
            continue

        hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 10))
        plt.subplot(len(sample_indices), 3, i * 3 + 1)
        plt.imshow(img_color)
        plt.text(10, 10, f'Original Image\nLabel: {label}', ha='left', va='top',fontsize=9,color='blue',bbox=dict(facecolor='white',boxstyle='round', alpha=0.5, edgecolor='none'))
        plt.axis('off')

        plt.subplot(len(sample_indices), 3, i * 3 + 2)
        plt.imshow(img_gray, cmap='gray')
        plt.text(10, 10, 'Grayscale Image', ha='left', va='top',fontsize=9,color='blue',bbox=dict(facecolor='white',boxstyle='round', alpha=0.5, edgecolor='none'))
        plt.axis('off')

        plt.subplot(len(sample_indices), 3, i * 3 + 3)
        plt.imshow(hog_image_rescaled, cmap='gray')
        plt.text(10, 10, 'HOG Visualization', ha='left', va='top',fontsize=9,color='blue',bbox=dict(facecolor='white',boxstyle='round', alpha=0.5, edgecolor='none'))
        plt.axis('off')

    plt.tight_layout()
    plt.show()

# Visualize HOG features
visualize_hog_samples(
    df=eda_df,
    img_path_col='imgPath',
    label_col='label',
    num_samples=5,
    target_size=(360, 360),
    hog_params=hog_params
)

Visualizing HOG Features on Sample Images...

We can see that the gradients are almost unnoticable without a clear direction on the clear background that do not have any angle changes.

Deep Feature Extraction using ViT

We want to extract deep learning features from the data using a pre-trained Vision Transformer (ViT) model, specifically google/vit-base-patch16-224-in21k, provided by Hugging Face. The objective is to transform high-dimensional image data into compact, meaningful feature vectors suitable for downstream tasks such as classification or clustering. We would like to use the ViT's ability to capture complex patterns in images, which is particularly valuable for blood cell analysis where distinguishing subtle morphological differences is critical.

# Load a pre-trained ViT model and feature extractor
def load_vit_model(model_name="google/vit-base-patch16-224-in21k"):
    try:
        feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
        model = ViTModel.from_pretrained(model_name).to(DEVICE)
        model.eval()  # Set to evaluation mode
        return feature_extractor, model
    except Exception as e:
        print(f"Error loading model {model_name}: {e}")
        return None, None

# Preprocess an image for ViT feature extraction
def preprocess_image(image_path, feature_extractor):
    try:
        img = Image.open(image_path).convert("RGB")
        inputs = feature_extractor(images=img, return_tensors="pt")
        return {k: v.to(DEVICE) for k, v in inputs.items()}
    except Exception as e:
        print(f"Error preprocessing {image_path}: {e}")
        return None

# Extract deep features from an image using ViT
def extract_deep_features(image_path, feature_extractor, model):
    inputs = preprocess_image(image_path, feature_extractor)
    if inputs is None:
        return None

    try:
        with torch.no_grad():
            outputs = model(**inputs)
            features = outputs.last_hidden_state.mean(dim=1).squeeze().cpu().numpy()
        return features
    except Exception as e:
        print(f"Error extracting features from {image_path}: {e}")
        return None

# Extract deep features for all images in a dataset
def extract_features_for_dataset(df, feature_extractor, model, image_path_column="imgPath"):
    deep_features_list = []
    valid_indices = []

    print("Extracting Deep Learning features...")
    for idx, img_path in enumerate(tqdm(df[image_path_column], desc="Processing images")):
        features = extract_deep_features(img_path, feature_extractor, model)
        if features is not None:
            deep_features_list.append(features)
            valid_indices.append(idx)

    if not deep_features_list:
        raise ValueError("No valid features extracted from the dataset.")

    deep_features = np.array(deep_features_list)
    labels = df.iloc[valid_indices]["label"].values

    print(f"Deep Features Extracted. Shape: {deep_features.shape}")
    if len(valid_indices) != len(df):
        print(f"Warning: Processed {len(valid_indices)} out of {len(df)} images successfully.")

    return deep_features, labels, valid_indices

# Load model and feature extractor
feature_extractor, model_vit = load_vit_model()

# Extract features
deep_features, labels, valid_indices = extract_features_for_dataset(
    df=df_filtered,
    feature_extractor=feature_extractor,
    model=model_vit,
    image_path_column="imgPath"
)

# Use features and labels for downstream tasks
print(f"Features shape: {deep_features.shape}, Labels shape: {labels.shape}")

Extracting Deep Learning features...

Processing images: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17092/17092 [20:42<00:00, 13.75it/s]

Deep Features Extracted. Shape: (17092, 768)
Features shape: (17092, 768), Labels shape: (17092,)

Now, we will visualizes nearest neighbors in the deep feature space of the images, utilizing features extracted from a Vision Transformer (ViT) model. The purpose is to evaluate the quality of the deep features by assessing whether visually and semantically similar images (same blood cell types) are grouped closely in the feature space. A k-Nearest Neighbors model with cosine similarity is employed to identify neighbors, and a visualization displays query images alongside their nearest neighbors.

---

Functions Explanation:

Feature Scaling:

• scale_features(features) Scales input features using scikit-learn's StandardScaler, fitting and transforming the data, and returns the scaled features and the scaler object.

Nearest Neighbors Model:

• fit_knn_model(features, n_neighbors=5, metric='cosine') Fits a scikit-learn NearestNeighbors model to find the nearest neighbors in the feature space, using a specified number of neighbors and distance metric (default cosine).

Query Selection:

• select_query_indices(n_samples, n_queries=3, random_seed=None) Randomly selects indices for query samples from the dataset, with an optional random seed for reproducibility.

Visualization:

• visualize_nearest_neighbors(df, features, nn, query_indices, n_neighbors, img_size, image_path_column='imgPath', label_column='label') Creates a grid of plots showing query images and their nearest neighbors, loaded from a DataFrame, with labels and cosine similarity.

Full Pipeline:

• run_nearest_neighbors_visualization(df, features, n_neighbors=5, n_queries=3, img_size=(360, 360), random_seed=None, image_path_column='imgPath', label_column='label') Runs a pipeline to visualize nearest neighbors: scales features, fits a KNN model, selects random query indices, and visualizes query images with their nearest neighbors.

# Scale features
def scale_features(features):
    scaler = StandardScaler()
    scaled_features = scaler.fit_transform(features)
    return scaled_features, scaler

# Fit a KNN model to find nearest neighbors
def fit_knn_model(features, n_neighbors=5, metric='cosine'):
    nn = NearestNeighbors(n_neighbors=n_neighbors + 1, metric=metric)
    nn.fit(features)
    return nn

# Select random query indices for visualization
def select_query_indices(n_samples, n_queries=3, random_seed=None):
    if random_seed is not None:
        np.random.seed(random_seed)
    return np.random.choice(n_samples, n_queries, replace=False)

# Visualize query images and their nearest neighbors
def visualize_nearest_neighbors(df, features, nn, query_indices, n_neighbors, img_size, image_path_column='imgPath', label_column='label'):
    plt.figure(figsize=(n_neighbors * 3, len(query_indices) * 3))
    plot_idx = 1

    for i, query_idx in enumerate(query_indices):
        query_path = df[image_path_column].iloc[query_idx]
        query_label = df[label_column].iloc[query_idx]
        query_feature = features[query_idx].reshape(1, -1)

        distances, indices = nn.kneighbors(query_feature)

        # Plot query image
        plt.subplot(len(query_indices), n_neighbors + 1, plot_idx)
        img = load_preprocess_image(query_path, target_size=img_size, color_mode='rgb')
        if img is not None:
            plt.imshow(img)
        plt.text(10, 10, f'Query: {query_label}', ha='left', va='top',fontsize=9,color='black',bbox=dict(facecolor='white',boxstyle='round', alpha=0.5, edgecolor='none'))

        plt.axis('off')
        plot_idx += 1

        # Plot neighbors
        for j in range(1, n_neighbors + 1):
            neighbor_idx = indices[0, j]
            neighbor_path = df[image_path_column].iloc[neighbor_idx]
            neighbor_label = df[label_column].iloc[neighbor_idx]

            plt.subplot(len(query_indices), n_neighbors + 1, plot_idx)
            img = load_preprocess_image(neighbor_path, target_size=img_size, color_mode='rgb')
            if img is not None:
                plt.imshow(img)
            plt.text(10, 10, f'Neighbor {j}\n{neighbor_label}', ha='left', va='top',fontsize=9,color='blue',bbox=dict(facecolor='white',boxstyle='round', alpha=0.5, edgecolor='none'))
            plt.axis('off')
            plot_idx += 1

    plt.suptitle('Nearest Neighbors in Deep Feature Space (Cosine Similarity)', fontsize=16,fontweight='bold')
    plt.tight_layout(rect=[0, 0.03, 1, 0.97])
    plt.show()

# Run the process
def run_nearest_neighbors_visualization(df, features, n_neighbors=5, n_queries=3, img_size=(360, 360), random_seed=None, image_path_column='imgPath', label_column='label'):
    print("Finding and Visualizing Nearest Neighbors in Deep Feature Space...")

    # Scale features
    scaled_features, _ = scale_features(features)

    # Fit KNN model
    nn = fit_knn_model(scaled_features, n_neighbors=n_neighbors)

    # Select query indices
    query_indices = select_query_indices(len(df), n_queries=n_queries, random_seed=random_seed)

    # Visualize
    visualize_nearest_neighbors(
        df=df,
        features=scaled_features,
        nn=nn,
        query_indices=query_indices,
        n_neighbors=n_neighbors,
        img_size=img_size,
        image_path_column=image_path_column,
        label_column=label_column
    )

# Run visualization
run_nearest_neighbors_visualization(
    df=df_filtered,
    features=deep_features,
    n_neighbors=5,
    n_queries=3,
    img_size=(360, 360),
    random_seed=42,
    image_path_column="imgPath",
    label_column="label"
)

Finding and Visualizing Nearest Neighbors in Deep Feature Space...

The figure above displays three query images and their five nearest neighbors, each labeled with their respective blood cell type. The images are preprocessed to 360x360 resolution, and neighbors are identified using cosine similarity in the scaled ViT feature space. The visualization reveals that neighbors share visual characteristics (e.g., shape, color) and class labels with the query, indicating the discriminative power of the features.

We now prepared for further analysis.

Dimensionality Reduction (PCA and t-SNE)

We will explore the resulting vectors. We will use dimensionality reduction (PCA, t-SNE) to present the data in two dimensions and identify structures, and perform clustering to discover natural groups in the data without considering the true labels. We will try to achieve a visual understanding of the data and the quality of the extracted features, identify patterns, and evaluate the potential for separating the different cell types. The purpose is to assess the separability of blood cell classes in the reduced feature space, providing insights into the discriminative power of the features for classification tasks.

Comparison of Dimensionality Reduction Methods: PCA and t-SNE.

In this section, we will examine and compare the dimensionality reduction results obtained from two different methods, PCA and t-SNE. The comparison will be performed on features extracted using different models (ViT and HOG) in order to understand how each method represents the data in low-dimensional space and what the separation between the different blood cell categories looks like.

We will start by examining the results for features extracted from the ViT model.

---

Functions Explanation:

Feature Scaling:

• scale_features(features) Scales input features using scikit-learn's StandardScaler, fitting and transforming the data, and returns the scaled features and the scaler object.

Dimensionality Reduction:

• apply_pca(features, variance_threshold=0.95, random_seed=None) Applies PCA to retain a specified percentage of variance (default 95%), transforming the features and returning the reduced features and PCA object.

• apply_tsne(features, n_components=2, perplexity=30, learning_rate=200, n_iter=1000, random_seed=None, max_input_dims=50) Applies t-SNE to reduce features to a specified number of dimensions (default 2), using a subset of input dimensions if necessary, and returns the transformed features and t-SNE object.

Visualization:

• visualize_dimensionality_reduction(features_pca, features_tsne, labels, feature_name, figsize=(1000, 500)) Creates side-by-side scatter plots comparing PCA and t-SNE results, visualizing the first two components (or one if fewer) with labels as colors.

Full Pipeline:

• run_dimensionality_reduction(features, labels, feature_name="Features", variance_threshold=0.95, max_tsne_dims=50, perplexity=30, learning_rate='auto', n_tsne_iter=1000, random_seed=None, figsize=(1200, 550)) Runs a dimensionality reduction pipeline: scales features, applies PCA, applies t-SNE on PCA-transformed features, and visualizes the results, handling edge cases like insufficient dimensions or samples.

def scale_features(features):
    scaler = StandardScaler()
    scaled_features = scaler.fit_transform(features)
    return scaled_features, scaler

# Apply PCA
def apply_pca(features, variance_threshold=0.95, random_seed=None):
    pca = PCA(n_components=variance_threshold, random_state=random_seed)
    features_pca = pca.fit_transform(features)
    return features_pca, pca

# Apply t-SNE to reduce features to specified dimensions
def apply_tsne(features, n_components=2, perplexity=30, learning_rate=200, n_iter=1000, random_seed=None, max_input_dims=50):
    n_dims = min(max_input_dims, features.shape[1])
    tsne = TSNE(
        n_components=n_components,
        perplexity=perplexity,
        learning_rate=learning_rate,
        # n_iter=n_iter,
        max_iter=n_iter,
        random_state=random_seed
    )
    features_tsne = tsne.fit_transform(features[:, :n_dims])
    return features_tsne, tsne

# Visualize PCA and t-SNE results using scatter plots
def visualize_dimensionality_reduction(features_pca, features_tsne, labels, feature_name, figsize=(1000, 500)):
    labels_str = [str(lbl) for lbl in labels]

    pca_dim = features_pca.shape[1]
    pca_x = features_pca[:, 0]
    pca_y = features_pca[:, 1] if pca_dim >= 2 else np.zeros_like(pca_x)
    pca_title_suffix = '(First 2 Components)' if pca_dim >=2 else '(First Component)'
    pca_yaxis_title = 'Principal Component 2' if pca_dim >= 2 else ''

    tsne_dim = features_tsne.shape[1]
    tsne_x = features_tsne[:, 0]
    tsne_y = features_tsne[:, 1] if tsne_dim >= 2 else np.zeros_like(tsne_x)
    tsne_yaxis_title = 't-SNE Component 2' if tsne_dim >= 2 else ''

    pca_title = f'PCA of {feature_name} {pca_title_suffix}'
    tsne_title = f't-SNE of {feature_name}'

    fig = make_subplots(
        rows=1, cols=2,
        subplot_titles=(pca_title, tsne_title),
        horizontal_spacing=0.1
    )

    temp_fig_pca = px.scatter(
        x=pca_x,
        y=pca_y, color_continuous_scale='viridis',
        color=labels_str,
        labels={'color': 'Label'},
        title="PCA Temp"
    )

    temp_fig_tsne = px.scatter(
        x=tsne_x,
        y=tsne_y,
        color=labels_str, color_continuous_scale='viridis',
        labels={'color': 'Label'},
        title="t-SNE Temp"
    )

    legend_labels_added = set()
    for trace in temp_fig_pca.data:
        label = trace.name
        trace.showlegend = (label not in legend_labels_added)
        fig.add_trace(trace, row=1, col=1)
        if trace.showlegend:
            legend_labels_added.add(label)

    # Add t-SNE traces
    for trace in temp_fig_tsne.data:
        trace.showlegend = False
        fig.add_trace(trace, row=1, col=2)

    fig.update_xaxes(title_text='Principal Component 1', row=1, col=1)
    fig.update_yaxes(title_text=pca_yaxis_title, row=1, col=1, showticklabels=(pca_dim >= 2), showline=(pca_dim >= 2), zeroline=(pca_dim >= 2))
    fig.update_xaxes(title_text='t-SNE Component 1', row=1, col=2)
    fig.update_yaxes(title_text=tsne_yaxis_title, row=1, col=2, showticklabels=(tsne_dim >= 2), showline=(tsne_dim >= 2), zeroline=(tsne_dim >= 2))

    fig.update_layout(
        title_text=f'Dimensionality Reduction Comparison ({feature_name})',
        title_font_size=20,
        title_x=0.5,
        width=figsize[0],
        height=figsize[1],
        legend_title_text='Label',
        legend=dict(
            traceorder='normal',
            itemsizing='constant'
        ),
        hovermode='closest'
    )

    fig.update_traces(
        marker=dict(size=8, opacity=0.7, line=dict(width=0.7, color='DarkSlateGrey')),
        selector=dict(mode='markers')
    )

    fig.show()

# Run dimensionality reduction for EDA
def run_dimensionality_reduction(features, labels, feature_name="Features", variance_threshold=0.95, max_tsne_dims=50,
                                     perplexity=30, learning_rate='auto', n_tsne_iter=1000, random_seed=None, figsize=(1200, 550)):
    print(f"\nPerforming EDA on {feature_name}...")

    # Scale features
    features_scaled, _ = scale_features(features)
    print(f"Features scaled. Shape: {features_scaled.shape}")

    # Apply PCA
    features_pca, pca = apply_pca(features_scaled, variance_threshold, random_seed)
    print(f"PCA completed. Original dims: {features_scaled.shape[1]}, Reduced dims: {pca.n_components_}. Output shape for viz: {features_pca.shape}")

    # Apply t-SNE
    n_pca_components = features_pca.shape[1]
    n_dims_for_tsne = min(max_tsne_dims, n_pca_components) # How many PCA dims to feed into t-SNE

    if n_dims_for_tsne < 2:
        print(f"\nWarning: Only {n_dims_for_tsne} PCA components available for t-SNE input. t-SNE may not be effective.")
        features_tsne = np.zeros((features_pca.shape[0], 2))
        print("Skipping t-SNE calculation due to insufficient input dimensions.")
    elif features.shape[0] <= perplexity:
        print(f"\nWarning: Number of samples ({features.shape[0]}) is less than or equal to perplexity ({perplexity}). Adjusting perplexity or skipping t-SNE.")
        print(f"\nPerforming t-SNE on the first {n_dims_for_tsne} PCA components of {feature_name}...")
        features_tsne, _ = apply_tsne(
            features_pca,
            n_components=2,
            perplexity=perplexity,
            learning_rate=learning_rate,
            n_iter=n_tsne_iter,
            random_seed=random_seed,
            max_input_dims=n_dims_for_tsne
        )
        print("t-SNE completed.")
    else:
        print(f"\nPerforming t-SNE on the first {n_dims_for_tsne} PCA components of {feature_name}...")
        features_tsne, _ = apply_tsne(
            features_pca,
            n_components=2,
            perplexity=perplexity,
            learning_rate=learning_rate,
            n_iter=n_tsne_iter,
            random_seed=random_seed,
            max_input_dims=n_dims_for_tsne
        )
        print("t-SNE completed.")

    visualize_dimensionality_reduction(
        features_pca=features_pca,
        features_tsne=features_tsne,
        labels=labels,
        feature_name=feature_name,
        figsize=figsize
    )

    return features_pca, features_tsne, pca

Upon execution, the code scales the input deep features, reduces their dimensionality using PCA (retaining 95% variance) and t-SNE (to 2 dimensions), and generates a side-by-side visualization of the results. The PCA plot displays the first two principal components, while the t-SNE plot shows a 2D embedding, both colored by class labels.

# Run EDA on deep features
deep_features_pca, deep_features_tsne, deep_features_fitted = run_dimensionality_reduction(features=deep_features,
                                                                                          labels=labels,feature_name="Deep Features",learning_rate=200, random_seed=SEED)

print(f"PCA result shape: {deep_features_pca.shape}")
print(f"t-SNE result shape: {deep_features_tsne.shape}")

Performing EDA on Deep Features...
Features scaled. Shape: (17092, 768)
PCA completed. Original dims: 768, Reduced dims: 117. Output shape for viz: (17092, 117)

Performing t-SNE on the first 50 PCA components of Deep Features...
t-SNE completed.

PCA result shape: (17092, 117)
t-SNE result shape: (17092, 2)

The above plot shows a comparison between PCA and t-SNE dimensionality reduction on features extracted from the ViT model for blood cell images. Note that you can click on the labels in the legend to hide different labels.

Several conclusions can be drawn:

Quality of the extracted features (ViT Features):
The t-SNE results show clear and relatively separate clusters for each of the 8 blood cell types. This indicates that the features that the ViT model learned to extract are informative and allow for good discrimination between the different blood cell types.

Comparison between PCA and t-SNE:

• PCA: The PCA plot shows significant overlap between the different groups. Although we can see a certain tendency for points of the same type to cluster together, the separation is not clear, and points of different types are mixed together, especially in the center of the plot. This suggests that the largest variance in the data (which PCA captures) does not necessarily best distinguish between linearly different categories in the first two dimensions.
• t-SNE: In contrast, the t-SNE plot shows much better separation between clusters. Each color (representing a blood cell type) forms a distinct and relatively dense group. t-SNE, which focuses on preserving local structures and similarities between nearby points in the high-dimensional feature space, is able to present the separation between groups more effectively in the two-dimensional space.

Data structure in the feature space:
The difference between the two results suggests that the relationships that distinguish between cell types in the feature space extracted by ViT are likely complex and nonlinear. PCA, being a linear technique, has difficulty capturing this structure in only two dimensions, while t-SNE, a nonlinear technique, is more successful in doing so.

Potential for Classification:
The clear separation observed in t-SNE indicates a high potential for successful classification of blood cells using the features extracted by ViT. It is reasonable to assume that a classifier based on these features will be able to achieve good performance.

Conclusion:
The features extracted by the ViT model contain a lot of information that allows for discrimination between the different blood cell types. While two-dimensional PCA fails to show a clear separation, t-SNE demonstrates that the data can be well separated in the feature space, probably in a nonlinear manner, indicating a high quality of the extracted features and good potential for the classification task.

Now, let's examine the results for features extracted from the HOG model.

# Run EDA on HOG features
hog_features_pca, hog_features_tsne, hog_features_fitted = run_dimensionality_reduction(features=hog_features,
                                                                                        labels=labels,
                                                                                        feature_name="HOG Features",learning_rate=200, random_seed=SEED)

print(f"PCA result shape: {hog_features_pca.shape}")
print(f"t-SNE result shape: {hog_features_tsne.shape}")

Performing EDA on HOG Features...
Features scaled. Shape: (17092, 44100)
PCA completed. Original dims: 44100, Reduced dims: 7095. Output shape for viz: (17092, 7095)

Performing t-SNE on the first 50 PCA components of HOG Features...
t-SNE completed.

PCA result shape: (17092, 7095)
t-SNE result shape: (17092, 2)

The plot above shows a comparison between PCA and t-SNE dimensionality reduction on features extracted from the ViT model for blood cell images. Note that you can click on the labels in the legend to hide different labels.

Now let's analyze the results for feature extraction using HOG, compared to what we saw earlier with ViT.

In the new image with HOG results:

PCA of HOG features:

• The graph shows almost complete mixing between all 8 blood cell types (colors).
• It is very difficult, if at all possible, to discern clear clusters or any separation between the different groups. The points appear scattered and significantly mixed.
• The two principal components of PCA fail to capture the variance that distinguishes the groups in the HOG features.

t-SNE of HOG features:

• The t-SNE graph also shows a very high level of mixing between the different groups.
• In contrast to the ViT results, here no clear and separate clusters are formed for each color. Most of the points are scattered and mixed with points of other colors.
• Although t-SNE tries to preserve local structures, it seems that the HOG features are not "separate" enough in the feature space for t-SNE to be able to create clear visual clusters from them in 2D.

Comparison and conclusions (HOG vs. ViT):

• Separation quality: The results with HOG show significantly worse separation between the groups compared to the results with ViT. While ViT (especially with t-SNE) showed clear clusters, HOG shows almost complete mixing in both methods.
• Feature Efficiency: The comparison clearly indicates that the ViT features (learned using deep learning) are much more informative and suitable for the task of classifying these blood cells than the HOG features (which are "classical" features calculated manually). ViT was able to learn representations that better discriminate between the cell types.
• Classification Potential: Based on these simulations, it is likely that a classifier based on HOG features will struggle to achieve high accuracy, certainly compared to a classifier based on ViT features.

In conclusion for HOG:
The simulations (both PCA and t-SNE) of the HOG features indicate that these features do not provide a good separation between the 8 blood cell types in this case. The results are considered not good in the context of demonstrating separation between the groups, highlighting the advantage of the learned ViT features over the classic HOG features for this specific classification task.

Explained Variance

We will examine the explained variance of the PCA method for the features extracted from the different models. We will focus on the proportion of variance that the method manages to preserve in each of the principal components, to assess its effectiveness in capturing the meaningful information in the data.

---

Function Explanation:

Feature Scaling:

• scale_features(features) Scales input features using scikit-learn's StandardScaler, fitting and transforming the data, and returns the scaled features and the scaler object.

PCA Analysis:

• compute_explained_variance(features_scaled, random_seed=None) Applies PCA to scaled features to capture all variance, returning the PCA object and the explained variance ratios for each component.

• plot_explained_variance(explained_variance_ratio, feature_name, variance_thresholds=[0.95, 0.99], width=1200, height=550) Creates a Plotly figure showing individual and cumulative explained variance ratios for PCA components, with horizontal and vertical lines for specified variance thresholds.

• select_pca_components(explained_variance_ratio, variance_threshold=0.95) Determines the number of PCA components needed to retain a specified percentage of variance (default 95%).

• apply_pca(features_scaled, n_components, random_seed=None) Applies PCA to scaled features with a specified number of components, returning the transformed features and the PCA object.

• run_pca_explained_variance(features, feature_name="Deep Features", variance_threshold=0.95, variance_thresholds_to_plot=[0.95, 0.99], random_seed=None) Performs a full PCA pipeline: scales features, computes and plots explained variance, selects components for a target variance, applies PCA, and returns the transformed features and number of components.

# Scale features
def scale_features(features):
    scaler = StandardScaler()
    scaled_features = scaler.fit_transform(features)
    return scaled_features, scaler

# Compute PCA to capture all variance and calculate explained variance ratios
def compute_explained_variance(features_scaled, random_seed=None):
    pca_full = PCA(random_state=random_seed)
    pca_full.fit(features_scaled)
    return pca_full, pca_full.explained_variance_ratio_

# Plot cumulative and individual explained variance ratio for PCA components
def plot_explained_variance(explained_variance_ratio, feature_name, variance_thresholds=[0.95, 0.99], width=1200, height=550):
    n_components_total = len(explained_variance_ratio)
    component_numbers = np.arange(1, n_components_total + 1)
    # Calculate cumulative explained variance
    cum_variance = np.cumsum(explained_variance_ratio)
    fig = go.Figure()

    fig.add_trace(
        go.Bar(
            x=component_numbers,
            y=explained_variance_ratio,
            name='Individual Variance',
            marker_color='lightblue',
            hovertemplate='Component %{x}<br>Individual Variance: %{y:.4f}<extra></extra>'
        )
    )

    fig.add_trace(
        go.Scatter(
            x=component_numbers,
            y=cum_variance,
            mode='lines+markers',
            line=dict(color='midnightblue', width=1),
            marker=dict(size=4, color='midnightblue'),
            name='Cumulative Variance',
            hovertemplate='Components: %{x}<br>Cumulative Variance: %{y:.4f}<extra></extra>'
        )
    )

    threshold_colors = ['#d62728', '#2ca02c', '#ff7f0e', '#9467bd']

    for i, thresh in enumerate(variance_thresholds):
        color = threshold_colors[i % len(threshold_colors)]

        # Find the number of components needed to reach the threshold
        try:
            n_components_needed = np.argmax(cum_variance >= thresh) + 1
            # Handle edge case where threshold is never met
            if cum_variance[n_components_needed-1] < thresh and n_components_needed == n_components_total:
                 n_components_needed = -1 # Indicate threshold not met
        except ValueError:
             n_components_needed = -1

        # Add horizontal line
        fig.add_hline(
            y=thresh,
            line=dict(color=color, dash="dash", width=1.5),
            annotation_text=f'{thresh*100:.0f}% Threshold',
            annotation_position="bottom right",
            annotation_font_size=10,
            annotation_font_color=color,
            name=f'{thresh*100:.0f}% Threshold'
        )

        # Add vertical line and annotation if threshold is met
        if n_components_needed > 0:
             # Add vertical line at crossing point
             fig.add_vline(
                 x=n_components_needed,
                 line=dict(color=color, dash="dash", width=1.5)
             )
             fig.add_annotation(
                 x=n_components_needed,
                 y=thresh,
                 text=f'{n_components_needed} comps ({thresh*100:.0f}%)',
                 showarrow=True,
                 arrowhead=1,
                 ax=-20 if n_components_needed > n_components_total * 0.1 else 20,
                 ay=-30 - i*10,
                 bordercolor="#c7c7c7",
                 borderwidth=1,
                 borderpad=4,
                 bgcolor="white",
                 opacity=0.8,
                 font=dict(size=10, color=color)
             )

    fig.update_layout(
        title=f'PCA Explained Variance ({feature_name})',
        xaxis_title='Number of Principal Components',
        yaxis_title='Explained Variance Ratio',
        yaxis_range=[0, 1.05],
        width=width,
        height=height,
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=1.02, # Position above plot
            xanchor="right",
            x=1
        ),
        hovermode='x unified',
        template='plotly_white',
        margin=dict(l=60, r=40, t=80, b=60)
    )

    fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='LightGray')
    fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='LightGray', tickformat='.0%')

    fig.show()

def select_pca_components(explained_variance_ratio, variance_threshold=0.95):
    """
    Determine number of PCA components to retain specified variance.
    """
    return np.argmax(np.cumsum(explained_variance_ratio) >= variance_threshold) + 1

def apply_pca(features_scaled, n_components, random_seed=None):
    """
    Apply PCA with specified number of components.
    """
    pca = PCA(n_components=n_components, random_state=random_seed)
    features_pca = pca.fit_transform(features_scaled)
    return features_pca, pca

def run_pca_explained_variance(features, feature_name="Deep Features", variance_threshold=0.95,
                                   variance_thresholds_to_plot=[0.95, 0.99], random_seed=None):
    print(f"\nPerforming PCA on {feature_name}...")

    # Scale features
    features_scaled, _ = scale_features(features)
    print(f"Features scaled. Shape: {features_scaled.shape}")

    # Compute explained variance
    pca_full, explained_variance_ratio = compute_explained_variance(features_scaled, random_seed)

    # Plot explained variance
    plot_explained_variance(
        explained_variance_ratio=explained_variance_ratio,
        feature_name=feature_name,
        variance_thresholds=variance_thresholds_to_plot,
    )

    # Select number of components
    n_components = select_pca_components(explained_variance_ratio, variance_threshold)
    print(f"Number of components to explain {int(variance_threshold*100)}% variance: {n_components}")

    # Apply PCA with selected components
    features_pca, pca = apply_pca(features_scaled, n_components, random_seed)
    print(f"PCA completed. Original dims: {features_scaled.shape[1]}, Reduced dims: {pca.n_components_}")

    return features_pca, n_components

# Run PCA explained variance analysis on deep features
explained_deep_features_pca, deep_n_components = run_pca_explained_variance(
    features=deep_features,
    feature_name="Deep Features",
    variance_threshold=0.95,
    variance_thresholds_to_plot=[0.95, 0.99],
    random_seed=SEED
)

Performing PCA on Deep Features...
Features scaled. Shape: (17092, 768)

Number of components to explain 95% variance: 117
PCA completed. Original dims: 768, Reduced dims: 117

The plot above shows the explained variance as a function of the number of Principal Components considered in PCA, for features extracted from the ViT model.

The following conclusions can be drawn:

Variance Concentration:
The first principal components capture a very large portion of the variance in the data. You can see that the dark blue curve (cumulative variance) rises very steeply at the beginning. For example, about 80% of the variance is explained by less than 20 first components.

Potential for Dimensionality Reduction:
The dimensionality of the data can be significantly reduced while retaining most of the information (as measured by variance).

• To preserve 95% of the original variance in the data, it is sufficient to use the 117 first principal components.
• To preserve 99% of the original variance in the data, 343 principal components are needed.
• The fact that 99% of the variance can be explained with 343 components out of the 750+ possible indicates that there are correlations and redundancy in the information in the original feature space.

The curve flattens as more components are added. This means that each additional component contributes less and less to explaining the total variance. Therefore, there is a point where adding more components is not very effective in terms of the amount of additional information (variance) they capture.

In addition, the graph explains why the 2D PCA visualization (as we saw earlier) did not show good separation between the groups. The first two components capture only a fraction of the total variance (perhaps 30-50%), and much of the information spread across the other components (which may be essential for separating groups) is lost in the reduction to just two dimensions. However, using a larger number of components (such as 117 or 343) may be useful for other tasks, such as building a classification model.

# Run PCA explained variance analysis on HOG features
explained_hog_features_pca, hog_n_components = run_pca_explained_variance(
    features=hog_features,
    feature_name="HOG Features",
    variance_threshold=0.95,
    variance_thresholds_to_plot=[0.95, 0.99],
    random_seed=SEED
)

Performing PCA on HOG Features...
Features scaled. Shape: (17092, 44100)

Number of components to explain 95% variance: 7095
PCA completed. Original dims: 44100, Reduced dims: 7095

The plot above shows the Explained Variance for the features extracted using HOG.

It can be concluded:

Very high dimensionality:
The HOG feature space is very high dimensional. The horizontal axis reaches almost 18,000 components, indicating that the feature vector generated by HOG for each image is very long (about 18,000 values).

Wide dispersion of variance:
Unlike ViT features, where the variance was relatively concentrated in the first few components, in HOG features the variance is spread over a very large number of components.

• To preserve 95% of the original variance, 7,095 principal components are required.
• To preserve 99% of the original variance, 11,318 principal components are required.

Lower efficiency of PCA for dimensionality reduction (while retaining high variance):
While PCA can be used, to retain a high percentage of the variance (like 95% or 99%), it requires retaining a huge number of components. Reducing the dimensionality to a small number of components (as we did with ViT) will result in the loss of a significant percentage of the total variance in the data.

Adaptation to clustering results:
The fact that the variance is spread over thousands of components explains why the 2D PCA visualization for HOG (which we saw earlier) showed such a large mixing between groups. The first two components capture only a very small percentage of the total variance, and therefore do not well represent the full structure of the data or the differences between groups.

Comparison with ViT:

• The original dimensionality of HOG is much higher.
• The variance in ViT features is much more concentrated in the first few components.
• PCA is much more efficient at compressing ViT features while preserving variance than it is at compressing HOG features.

Conclusion:
HOG features create a very high dimensional space where the variance is spread across many components. This makes dimensionality reduction using PCA less efficient if the goal is to preserve a high percentage of the original variance, and reinforces the conclusion that the features learned by ViT were more compact and informative for this task.

Dataset Split

Prepare DataLoader

Data Transformation Pipeline for Image Preprocessing

We define a set of image transformation pipelines using the torchvision.transforms module, tailored for preprocessing images. These transformations are designed to prepare image data for model training by resizing, normalizing, and applying augmentations, while also enabling experimentation to identify the most suitable configuration for the dataset, memory constraints, and runtime efficiency in the context of evaluating running performance.

Transformation Configurations:

• transform_resize_normalize: Resizes images to 224x224 pixels and converts them to tensors without normalization, serving as a minimal baseline.
• transform_normalize: Resizes images to 360x363 pixels and converts them to tensors, testing a different resolution for memory and runtime efficiency.
• transform_color: Resizes images to 224x224, converts to tensors, and applies RGB normalization to preserve color information, suitable for color-dependent models.
• transform_grayscale: Resizes images to 224x224, converts to grayscale, applies random horizontal and vertical flips for augmentation, converts to tensors, and normalizes using grayscale parameters, reducing memory usage by eliminating color channels.

The transformations are designed to preprocess images efficiently while balancing data fidelity and computational constraints. By defining multiple pipelines, the code facilitates comparative analysis to determine which transformation best suits the dataset and Colab's memory and runtime limitations. This also supports evaluating running performance, ensuring the selected pipeline optimizes model training within the given resource constraints.

The default transformation applied is transform_resize_normalize, providing a minimal preprocessing baseline for initial testing.

# Transform values
small_size = (128, 128)
new_size = (224, 224)
rgb_mean = [0.485, 0.456, 0.406]
rgb_std = [0.229, 0.224, 0.225]
grayscale_mean = [0.5]
grayscale_std = [0.5]

# Minimal transform - resize and normalization
transform_resize_normalize = transforms.Compose([
    transforms.Resize(new_size),
    transforms.ToTensor()
])

# Minimal transform - resize and normalization
transform_normalize = transforms.Compose([
    transforms.Resize((360, 363)),
    transforms.ToTensor()
])

# Size reduction, basic augmentation, normalization (color preservation)
transform_color = transforms.Compose([
    transforms.Resize(new_size),
    transforms.ToTensor(),
    transforms.Normalize(mean=rgb_mean, std=rgb_std)
])

# Size reduction, converting to grayscale, augmentation, normalization
transform_grayscale = transforms.Compose([
    transforms.Resize(new_size),
    transforms.Grayscale(num_output_channels=1),
    transforms.RandomHorizontalFlip(p=0.5),
    transforms.RandomVerticalFlip(p=0.5),
    transforms.ToTensor(),
    transforms.Normalize(mean=grayscale_mean, std=grayscale_std)
])

transform = transform_resize_normalize

Data Splitting Strategy
We divide our dataset into training (70%), validation (15%), and test (15%) sets. The dataset labels distribution is imbalanced, meaning some classes appear more frequently than others. To prevent our machine learning model from inheriting this class imbalance, we employ a 'stratified split'. Stratified splitting ensures that each subset (training, validation, and test) maintains the original proportions of labels. This approach guarantees that our model will be trained, validated, and tested on representative samples that reflect the true underlying class distribution. Also, we use the PyTorch DataLoader to efficiently manage our data batches, enabling efficient data loading and processing during model training.

# Check if 'df' is already defined
if 'df' not in locals() and 'df' not in globals():
    # Load the data only if 'df' is not already loaded
    df = load_dataframe('main_DataFrame')
else:
    print("DataFrame 'df' is already loaded.\n")

# For debugging only
debug = False
if debug==True:
  # df = df.sample(frac=0.1, random_state=42)
  dummy_df = sample_n_per_label(df, 50)
  train_df, temp_df = train_test_split(dummy_df, test_size=0.3, stratify=dummy_df['label'])
  val_df, test_df = train_test_split(temp_df, test_size=0.5, stratify=temp_df['label'])
else:
  # Perform stratified splitting
  # Split ratio: 70% training, 15% validation, 15% test
  train_df, temp_df = train_test_split(df, test_size=0.3, stratify=df['label'])
  val_df, test_df = train_test_split(temp_df, test_size=0.5, stratify=temp_df['label'])

# Create datasets for each split
train_dataset = ImageDataset(train_df, transform=transform)
val_dataset = ImageDataset(val_df, transform=transform)
test_dataset = ImageDataset(test_df, transform=transform)

# Create dataloaders
batch_size = 32
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# Print out the number of images in each set
print(f'\nTrain set size: {len(train_dataset)}')
print(f'Validation set size: {len(val_dataset)}')
print(f'Test set size: {len(test_dataset)}')
print('-' * 50)
print(f"Length of train dataloader: {len(train_loader)} batches of {batch_size}")
print(f"Length of validation dataloader: {len(val_loader)} batches of {batch_size}")
print(f"Length of test dataloader: {len(test_loader)} batches of {batch_size}")

DataFrame 'df' is already loaded.


Train set size: 11964
Validation set size: 2564
Test set size: 2564
--------------------------------------------------
Length of train dataloader: 374 batches of 32
Length of validation dataloader: 81 batches of 32
Length of test dataloader: 81 batches of 32

Train Validation and Test Datasets Exploration

we want to explore the train val and test datasets. lets perform train validation test analysis for proportion

These steps will allow us to:

• Check label distributions and verify that your transformations are applied correctly.
• Inspect individual samples.
• Visualize multiple images with their labels.
• Confirm that the train, validation and test dataset structure are set up as expected.

Inspect datasets proportions
lets make sure the proportions are ok

# Check the proportions of the splits relative to the original dataframe
def check_split_proportions(original_df, *splits):
    total_rows = len(original_df)
    proportions = {}
    for split_name, split_df in splits:
        split_size = len(split_df)
        proportions[split_name] = split_size / total_rows
    return proportions

# Proportions for train_df, temp_df, val_df, test_df
splits = [
    ('train_df', train_df),
    ('temp_df', temp_df),
    ('val_df', val_df),
    ('test_df', test_df)
]

proportions = check_split_proportions(df, *splits)

# Output the proportions of each split relative to the original dataframe
for split_name, proportion in proportions.items():
    print(f"{split_name}: {proportion:.4f}")

train_df: 0.7000
temp_df: 0.3000
val_df: 0.1500
test_df: 0.1500

let's see the Label Counts and Proportions Across Splits (for train_df, temp_df, val_df, test_df)

def visualize_dataset_splits(train_df, val_df, test_df, label_column='label'):
    train_df['split'] = 'Train'
    val_df['split'] = 'Validation'
    test_df['split'] = 'Test'

    combined_df = pd.concat([train_df, val_df, test_df], ignore_index=True)

    label_counts = combined_df.groupby(['split', label_column]).size().reset_index(name='count')
    label_props = label_counts.groupby('split')['count'].transform(lambda x: x / x.sum())
    label_counts['proportion'] = label_props

    color_map = ['#003f5c', '#2f4b7c', '#665191', '#a05195', '#d45087', '#f95d6a', '#ff7c43', '#ffa600']
    color_map2 = {
        'neutrophil': '#E63946',
        'eosinophil': '#2A9D8F',
        'ig': '#457B9D',
        'platelet': '#6A4C93',
        'erythroblast': '#F4A261',
        'monocyte': '#E9C46A',
        'basophil': '#264653',
        'lymphocyte': '#F77F00'
    }

    fig = make_subplots(rows=1, cols=2, subplot_titles=('Label Counts Across Splits', 'Label Proportions Across Splits'))

    count_fig = px.bar(label_counts,
                       x='split',
                       y='count',
                       color=label_column,
                       barmode='group',
                       labels={'count': 'Label Count', 'split': 'Dataset Split'},
                       opacity=0.9,
                       color_discrete_sequence=color_map
                      )
    for trace in count_fig.data:
        fig.add_trace(trace, row=1, col=1)

    prop_fig = px.bar(label_counts,
                      x='split',
                      y='proportion',
                      color=label_column,
                      barmode='group',
                      labels={'proportion': 'Label Proportion', 'split': 'Dataset Split'},
                      opacity=0.9,
                      color_discrete_sequence=color_map
                     )
    prop_fig.update_yaxes(tickformat='.0%')

    for trace in prop_fig.data:
        trace.update(showlegend=False)

    for trace in prop_fig.data:
        fig.add_trace(trace, row=1, col=2)

    # Update layout
    fig.update_layout(
        title_text='Label Counts and Proportions Across Splits',
        title_font_size=20,
        barmode='group',
        height=600,
        width=1100,
        template='plotly_white'
    )

    fig.update_yaxes(
        title_text='Count',
        row=1,
        col=1
    )

    fig.update_yaxes(
        title_text='Proportion',
        tickformat='.0%',
        row=1,
        col=2
    )

    fig.show()

visualize_dataset_splits(train_df, val_df, test_df, label_column='label')

The provided plot illustrating the distribution of different cell type labels across training, validation, and test dataset splits. The left chart shows the absolute counts, indicating that the training set contains a substantially larger number of examples for each cell type compared to the validation and test sets. The right chart, however, shows the proportions of each label within each split. Notably, these proportions are very consistent across all three splits, with cell types like neutrophils and eosinophils maintaining similar relative frequencies (around 20% and 18% respectively) in the training, validation, and test sets. This consistent proportional distribution suggests that the data was split effectively, likely using stratification, ensuring each subset is representative of the overall data's class balance.

Inspect Dataset Size
We want to check how many samples are in the training, validation and test datasets.

# Print split length
print("Training dataset length:", len(train_dataset))
print("Validation dataset length:", len(val_dataset))
print("Test dataset length:", len(test_dataset))

Training dataset length: 11964
Validation dataset length: 2564
Test dataset length: 2564

# Get class distribution
def get_class_distribution(dataset):
    class_counts = {}
    for _, label in dataset:
        class_name = dataset.idx_to_class[label]
        class_counts[class_name] = class_counts.get(class_name, 0) + 1

    return class_counts

# Get class distribution for each dataset
train_class_distribution = get_class_distribution(train_dataset)
val_class_distribution = get_class_distribution(val_dataset)
test_class_distribution = get_class_distribution(test_dataset)

# Print class distributions
print("Training dataset class distribution:\n", train_class_distribution)
print("\nValidation dataset class distribution:\n", val_class_distribution)
print("\nTest dataset class distribution:\n", test_class_distribution)

Training dataset class distribution:
 {'neutrophil': 2330, 'ig': 2026, 'eosinophil': 2182, 'platelet': 1643, 'lymphocyte': 850, 'monocyte': 994, 'erythroblast': 1086, 'basophil': 853}

Validation dataset class distribution:
 {'lymphocyte': 182, 'erythroblast': 232, 'neutrophil': 500, 'eosinophil': 467, 'platelet': 352, 'ig': 435, 'basophil': 183, 'monocyte': 213}

Test dataset class distribution:
 {'neutrophil': 499, 'ig': 434, 'basophil': 182, 'erythroblast': 233, 'eosinophil': 468, 'platelet': 353, 'monocyte': 213, 'lymphocyte': 182}

Examine a Single Sample
We want to view a single sample (image and label) from the training, validation and test datasets.

def plot_one_sample(train, val, test):
    datasets = [("Train", train), ("Validation", val), ("Test", test)]
    fig, axes = plt.subplots(1, 3, figsize=(10, 4))

    for i, (title, dataset) in enumerate(datasets):
        image, label = dataset[0]
        image = image.permute(1, 2, 0).numpy()
        class_name = dataset.idx_to_class[label]
        axes[i].imshow(image)
        axes[i].set_title(f"{title} Dataset\nClass = '{class_name}'")
        axes[i].axis("off")

    plt.tight_layout()
    plt.show()

plot_one_sample(train_dataset, val_dataset, test_dataset)

Examine Multiple Samples
We want to examine multiple samples (image and label) from the training, validation and test datasets.

def plot_multiple_samples(train, val, test, num_samples=8):
    datasets = [("Train", train), ("Validation", val), ("Test", test)]
    fig, axes = plt.subplots(3, num_samples, figsize=(num_samples * 2, 6))

    for row, (title, dataset) in enumerate(datasets):
        for col in range(num_samples):
            image, label = dataset[col]
            image = image.permute(1, 2, 0).numpy()
            axes[row, col].imshow(image)
            axes[row, col].set_title(dataset.idx_to_class[label])
            axes[row, col].axis("off")
        axes[row, 0].set_ylabel(title, fontsize=12, labelpad=10)

    plt.tight_layout()
    plt.show()


plot_multiple_samples(train_dataset, val_dataset, test_dataset)

Check Image Shape and Transformations
We want to verify that the image transformations are applied correctly (e.g., resizing and normalizing), so we need to inspect the shape of an image after applying the transformation.

# Fetch a sample
train_image, train_label = train_dataset[0]
val_image, val_label = val_dataset[0]
test_image, test_label = test_dataset[0]

# Should show [C, H, W] format (Channels, Height, Width)
print(f"Train image shape: {train_image.shape}")
print(f"Validation image shape: {val_image.shape}")
print(f"Test image shape: {test_image.shape}")

Train image shape: torch.Size([3, 224, 224])
Validation image shape: torch.Size([3, 224, 224])
Test image shape: torch.Size([3, 224, 224])

Data Loaders Exploration

We want to explore:

• train_loader
• val_loader
• test_loader

First, we want to examine the structure and types of the batches in the test_loader DataLoader.

# Inspect the structure and types of the batches in the DataLoader
for batch in test_loader:
    print(f"Batch type: {type(batch)}")
    if isinstance(batch, (tuple, list)):
        print("Element types:")
        for i, element in enumerate(batch):
            print(f"  Element {i}: {type(element)}")

    elif isinstance(batch, dict):
        print("Keys and types:")
        for key, value in batch.items():
            print(f"  Key '{key}': {type(value)}")

    break

Batch type: <class 'list'>
Element types:
  Element 0: <class 'torch.Tensor'>
  Element 1: <class 'torch.Tensor'>

PyTorch Color Channels
When color_channels=3, images are represented by pixel values corresponding to the red, green, and blue components, commonly referred to as the RGB color model. The tensor order is typically denoted as CHW (Color Channels, Height, Width), although there is ongoing debate regarding whether images should be represented in CHW (channels first) or HWC (channels last) format.
Additionally, the formats NCHW and NHWC are used, where N represents the number of images in a batch. For example, with a batch_size=32, the tensor shape would be [32, 1, 360, 360]. PyTorch defaults to the NCHW format (channels first) for many operations, although it is noted that NHWC (channels last) is often more efficient and is considered best practice.

For the current context, where both the dataset and models are relatively small, the choice of format may not significantly impact performance. However, this consideration becomes more important when working with larger datasets and convolutional neural networks.
Now, let's visualize a few random samples from the train_loader:

Examine Multiple Samples From the DataLoaders
We want to examine multiple samples from the training, validation and test DataLoaders.

plt.figure(figsize=(16, 12))

data_iter = iter(train_loader)
images, labels = next(data_iter)
grid_img = make_grid(images)
plt.imshow(grid_img.permute(1, 2, 0)) # Convert CHW (Color Channels, Height, Width) to HWC

formatted_labels = []
for i, label in enumerate(labels, 1):
    class_name = train_dataset.idx_to_class[label.item()]
    formatted_labels.append(f"'{class_name}'")
    if i % 8 == 0:
        formatted_labels.append('\n')

plt.title(f"Sample Labels:\n {', '.join(formatted_labels)}", fontsize=14)
plt.axis('off')
plt.tight_layout()
plt.show()

What's inside the DataLoaders (shape and labels)

# Check out what's inside the training dataloader
train_features_batch, train_labels_batch = next(iter(train_loader))
val_features_batch, val_labels_batch = next(iter(val_loader))
test_features_batch, test_labels_batch = next(iter(test_loader))

print("DataLoaders Type:")
print(f"Train DataLoader features type: {type(train_features_batch)}")
print(f"Train DataLoader labels type: {type(train_labels_batch)}")
print("")
print("DataLoaders Shape:")
print(f"Train DataLoader shape: {train_features_batch.shape}")
print(f"Validation DataLoader shape: {val_features_batch.shape}")
print(f"Test DataLoader shape: {test_features_batch.shape}")
print("")
print("DataLoaders Labels:")
print(f"Train DataLoader labels:\n{train_labels_batch}\n")
print(f"Validation DataLoader labels:\n{val_labels_batch}\n")
print(f"Test DataLoader labels:\n{test_labels_batch}\n")

DataLoaders Type:
Train DataLoader features type: <class 'torch.Tensor'>
Train DataLoader labels type: <class 'torch.Tensor'>

DataLoaders Shape:
Train DataLoader shape: torch.Size([32, 3, 224, 224])
Validation DataLoader shape: torch.Size([32, 3, 224, 224])
Test DataLoader shape: torch.Size([32, 3, 224, 224])

DataLoaders Labels:
Train DataLoader labels:
tensor([0, 5, 7, 1, 1, 6, 7, 3, 3, 2, 3, 7, 6, 7, 7, 1, 0, 0, 3, 2, 3, 0, 6, 7, 2, 6, 7, 7, 2, 1, 2, 6])

Validation DataLoader labels:
tensor([4, 2, 2, 6, 1, 6, 7, 6, 1, 3, 2, 1, 6, 3, 6, 6, 2, 0, 6, 1, 3, 1, 4, 5, 7, 1, 1, 6, 7, 1, 1, 6])

Test DataLoader labels:
tensor([6, 6, 3, 6, 0, 3, 0, 6, 2, 1, 7, 3, 6, 6, 6, 1, 1, 7, 1, 7, 3, 2, 1, 7, 3, 3, 2, 1, 0, 2, 0, 1])

Finally, we clear the memory of any unnecessary data:

# Delete datasets for memory efficiency
# del df
del eda_df
torch.cuda.empty_cache()  # Clear cached memory
gc.collect()              # Force garbage collection

248502

Logistic Regression via ViT Features

Having successfully employed a Vision Transformer (ViT) to process the blood cell images and extract meaningful feature representations, the next critical step is to utilize these features for the actual classification task. What we aim to achieve in this section is to classify the input images into one of the eight distinct blood cell categories present in our dataset.

Why we are taking this specific approach is twofold. Firstly, ViTs are renowned for their ability to capture intricate spatial hierarchies and complex patterns within images, resulting in rich, high-dimensional feature vectors that ideally encapsulate the discriminative visual characteristics of each blood cell type. Secondly, we employ Logistic Regression as the classifier. While simpler compared to end-to-end deep learning models, Logistic Regression is a robust, interpretable, and computationally efficient algorithm. Applying it to the powerful ViT features allows us to (a) leverage the feature extraction capabilities of the deep learning model and (b) evaluate how linearly separable the classes are within this learned feature space. Success with this method would validate the quality and discriminative power of the ViT-extracted features.

How we will proceed involves the following steps: We will first prepare our data by selecting relevant features by the pre-trained ViT for each image in our training and testing datasets. Next, we will split the data into training and testing sets to evaluate our model's performance. We will then fit a logistic regression model to the training data, interpret the model coefficients, and assess its accuracy using appropriate metrics. Finally, we will visualize the results to gain further insights into the relationships within our data.

Configuration

# Returns configuration dictionary
def get_config():
    return {
        'NUM_CLASSES': 8,
        'IMG_HEIGHT': 224,
        'IMG_WIDTH': 224,
        'VIT_INPUT_SIZE': 224,
        'VIT_BATCH_SIZE': 32,
        'LR_BATCH_SIZE': 64,
        'LR_EPOCHS': 50,
        'LR_LEARNING_RATE': 0.001,
        'DEVICE': torch.device("cuda" if torch.cuda.is_available() else "cpu"),
        'DT_MAX_DEPTH': 10,  # Decision tree max depth
        'DT_MIN_SAMPLES_SPLIT': 2,  # Minimum samples to split a node
        'DT_RANDOM_STATE': 42  # Random seed for reproducibility
    }

# Prints configuration settings
def print_config(config):
    print(f"Using device: {config['DEVICE']}")
    print(f"ViT Batch Size: {config['VIT_BATCH_SIZE']}")
    print(f"LR Batch Size: {config['LR_BATCH_SIZE']}")
    print(f"LR Epochs: {config['LR_EPOCHS']}")
    print(f"LR Learning Rate: {config['LR_LEARNING_RATE']}")

target_names = train_loader.dataset.classes
print(f"Class names: {target_names}")

Class names: ['basophil', 'eosinophil', 'erythroblast', 'ig', 'lymphocyte', 'monocyte', 'neutrophil', 'platelet']

Utility Functions

ViT Model Setup:

• setup_vit_model(config) Loads a pre-trained Vision Transformer (ViT) model, removes its classification head for feature extraction, sets it to evaluation mode, and moves it to the specified device.

• get_vit_transform(weights, vit_input_size) Creates an image transformation pipeline for ViT input, including resizing and normalization based on the model's pre-trained weights.

• plot_roc_and_threshold_curves(y_true, y_probs, num_classes, target_names, title_suffix="") Generates subplots showing ROC curves and TPR/FPR versus threshold curves for each class and micro-average, with AUC scores.

# ViT Model Setup
def setup_vit_model(config):
    """Loads and configures pre-trained ViT model for feature extraction."""
    print("\nLoading pre-trained ViT model...")
    weights = models.ViT_B_16_Weights.IMAGENET1K_V1
    vit_model = models.vit_b_16(weights=weights)
    feature_dim = vit_model.hidden_dim  # 768 for ViT-Base
    vit_model.heads.head = nn.Identity()  # Remove classification head
    vit_model.to(config['DEVICE'])
    vit_model.eval()  # Set to evaluation mode
    print(f"ViT model loaded and modified for feature extraction (output dim: {feature_dim}).")
    return vit_model, feature_dim

def get_vit_transform(weights, vit_input_size):
    """Defines image transformation for ViT input."""
    preprocess = weights.transforms()
    return transforms.Compose([
        transforms.Resize((vit_input_size, vit_input_size), antialias=True),
        transforms.Normalize(mean=preprocess.mean, std=preprocess.std),
    ])

# Plots ROC curves and TPR/FPR for every threshold curves
def plot_roc_and_threshold_curves(y_true, y_probs, num_classes, target_names, title_suffix=""):
    l_width = 2 # line width

    # Input validation
    if len(target_names) != num_classes:
        raise ValueError(f"Length of target_names ({len(target_names)}) must equal num_classes ({num_classes}).")
    if y_probs.shape[1] != num_classes:
         raise ValueError(f"Number of columns in y_probs ({y_probs.shape[1]}) must equal num_classes ({num_classes}).")
    y_true = np.asarray(y_true)
    if y_true.shape[0] != y_probs.shape[0]:
        raise ValueError(f"Number of samples in y_true ({y_true.shape[0]}) must match y_probs ({y_probs.shape[0]}).")

    # True labels
    y_true_bin = label_binarize(y_true, classes=list(range(num_classes)))
    if num_classes == 2 and y_true_bin.shape[1] == 1:
        y_true_bin = np.hstack((1 - y_true_bin, y_true_bin))
    elif num_classes > 1 and y_true_bin.shape[1] != num_classes:
         raise ValueError(f"Binarized y_true shape {y_true_bin.shape} is inconsistent with num_classes {num_classes}. Ensure y_true contains labels from 0 to num_classes-1.")

    # Compute ROC curve and ROC area for each class
    fpr = dict()
    tpr = dict()
    thresholds = dict()
    roc_auc = dict()

    for i in range(num_classes):
        if np.sum(y_true_bin[:, i]) > 0:
            fpr[i], tpr[i], thresholds[i] = roc_curve(y_true_bin[:, i], y_probs[:, i])
            roc_auc[i] = auc(fpr[i], tpr[i])
            thresholds[i][0] = thresholds[i][1] + (thresholds[i][1] - thresholds[i][2]) if len(thresholds[i])>2 else 1.0
            thresholds[i] = np.nan_to_num(thresholds[i])
        else:
            fpr[i], tpr[i], thresholds[i] = np.array([0]), np.array([0]), np.array([0])
            roc_auc[i] = float('nan')
            print(f"Warning: Class '{target_names[i]}' (index {i}) has no true samples in y_true. ROC/Threshold curves cannot be computed.")

    # Compute micro-average ROC curve and ROC area
    fpr["micro"], tpr["micro"], thresholds["micro"] = roc_curve(y_true_bin.ravel(), y_probs.ravel())
    roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
    thresholds["micro"][0] = thresholds["micro"][1] + (thresholds["micro"][1] - thresholds["micro"][2]) if len(thresholds["micro"])>2 else 1.0
    thresholds["micro"] = np.nan_to_num(thresholds["micro"])

    fig = make_subplots(
        rows=1, cols=2,
        subplot_titles=("ROC Curve", "TPR and FPR at every threshold")
    )
    color_palette = {
        'train': '#4B0082',
        'val': '#FF6347',
        'precision': '#F72585',
        'recall': '#4361EE',
        'f1': '#3A0CA3'
    }
    colors_1 = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',
              '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']
    colors = ['#636EFA', '#EF553B', '#00CC96', '#AB63FA', '#FFA15A',
              '#19D3F3', '#FF6692', '#B6E880', '#FF97FF', '#FECB52']

    # ROC Curves
    fig.add_shape(
        type='line', line=dict(dash='dash', color='black', width=l_width+1),
        x0=0, x1=1, y0=0, y1=1, row=1, col=1
    )

    for i in range(num_classes):
        color = colors[i % len(colors)]
        class_name = target_names[i]
        if not np.isnan(roc_auc[i]):
             roc_hover_text = [f"Threshold: {thr:.3f}<br>FPR: {fp:.3f}<br>TPR: {tp:.3f}"
                               for thr, fp, tp in zip(thresholds[i], fpr[i], tpr[i])]
             fig.add_trace(go.Scatter(
                 x=fpr[i],
                 y=tpr[i],
                 mode='lines',
                 line=dict(color=color, width=l_width),
                 # This trace will appear in the legend
                 name=f'{class_name}<br>(AUC={roc_auc[i]:.3f})',
                 legendgroup=f'class_{i}',
                 showlegend=True,
                 hoverinfo='text+name',
                 text=roc_hover_text
             ), row=1, col=1)
        else:
             fig.add_trace(go.Scatter(
                 x=[0], y=[0], mode='markers', marker=dict(color=color, size=l_width),
                 name=f'{class_name} (Not plotted)',
                 legendgroup=f'class_{i}',
                 showlegend=True
             ), row=1, col=1)

    micro_roc_hover_text = [f"Threshold: {thr:.3f}<br>FPR: {fp:.3f}<br>TPR: {tp:.3f}"
                            for thr, fp, tp in zip(thresholds["micro"], fpr["micro"], tpr["micro"])]
    fig.add_trace(go.Scatter(
        x=fpr["micro"],
        y=tpr["micro"],
        mode='lines',
        line=dict(color='#FF6347', width=l_width+1, dash='dot'),
        name=f'Micro-Average<br>(AUC={roc_auc["micro"]:.3f})',
        legendgroup='micro',
        showlegend=True,
        hoverinfo='text+name',
        text=micro_roc_hover_text
    ), row=1, col=1)

    # TPR/FPR
    for i in range(num_classes):
        color = colors[i % len(colors)]
        class_name = target_names[i]
        if not np.isnan(roc_auc[i]) and len(thresholds[i]) > 1:
             thresh_hover_text = [f"Threshold: {thr:.3f}<br>TPR: {tp:.3f}<br>FPR: {fp:.3f}"
                                  for thr, tp, fp in zip(thresholds[i], tpr[i], fpr[i])]

             # TPR vs Threshold
             fig.add_trace(go.Scatter(
                 x=thresholds[i],
                 y=tpr[i],
                 mode='lines',
                 line=dict(color=color, width=l_width),
                 name=f'TPR {class_name}',
                 legendgroup=f'class_{i}',
                 showlegend=False,
                 hoverinfo='text+name',
                 text=thresh_hover_text
             ), row=1, col=2)

             # FPR and Threshold
             fig.add_trace(go.Scatter(
                 x=thresholds[i],
                 y=fpr[i],
                 mode='lines',
                 line=dict(color=color, width=l_width, dash='dash'),
                 name=f'FPR {class_name}',
                 legendgroup=f'class_{i}',
                 showlegend=False, # Hide from legend
                 hoverinfo='text+name',
                 text=thresh_hover_text
             ), row=1, col=2)

    # Configure Layout for Subplots
    fig.update_xaxes(title_text="False Positive Rate (FPR)", range=[-0.02, 1.0], row=1, col=1)
    fig.update_yaxes(title_text="True Positive Rate (TPR)", range=[0.0, 1.05], scaleanchor="x", scaleratio=1, row=1, col=1)
    fig.update_xaxes(title_text="Decision Threshold", range=[-0.02, 1.02], row=1, col=2)
    fig.update_yaxes(title_text="Rate (TPR / FPR)", range=[0.0, 1.05], row=1, col=2)
    fig.update_xaxes(showspikes=True, spikesnap='cursor', spikemode='across', spikedash='dot', spikecolor='grey', spikethickness=1)
    fig.update_yaxes(showspikes=True, spikesnap='cursor', spikemode='across', spikedash='dot', spikecolor='grey', spikethickness=1)

    plot_title = "Model Performance Analysis"
    if title_suffix:
        plot_title += f" - {title_suffix}"

    fig.update_layout(
        title=plot_title,
        height=650,
        width=1350,
        hovermode='closest',
        # Legend Configuration
        legend=dict(
            yanchor="top",
            y=1,
            xanchor="left",
            x=1.01,
            orientation="v",
            tracegroupgap=15
        ),
        margin=dict(l=50, r=15, t=90, b=50)
    )

    return fig

Feature Extraction:

• extract_features(loader, model, transform, device) Extracts features from a DataLoader using the ViT model, applying transformations and returning features and labels as NumPy arrays.

• extract_all_features(train_loader, val_loader, test_loader, vit_model, image_transform, device) Extracts features for train, validation, and test sets using the ViT model, logging the process and returning feature-label pairs for each set.

• plot_tsne(X_test_np, y_test_np, num_classes, n_samples_tsne=None) Visualizes test set features using t-SNE, optionally subsampling data, and generates a scatter plot with class labels.

# Feature Extraction
def extract_features(loader, model, transform, device):
    """Extracts features from a DataLoader using the ViT model."""
    all_features = []
    all_labels = []
    with torch.no_grad():
        for inputs, labels in tqdm(loader, desc="Extracting features"):
            inputs = inputs.to(device)
            inputs = transform(inputs)
            features = model(inputs)
            all_features.append(features.cpu().numpy())
            all_labels.append(labels.cpu().numpy())
    return np.concatenate(all_features, axis=0), np.concatenate(all_labels, axis=0)

def extract_all_features(train_loader, val_loader, test_loader, vit_model, image_transform, device):
    """Extracts features for train, validation, and test sets."""
    print("\nExtracting features from Train set...")
    start_time = time.time()
    X_train_feat_np, y_train_np = extract_features(train_loader, vit_model, image_transform, device)
    print(f"Train features extracted. Shape: {X_train_feat_np.shape}. Time: {time.time() - start_time:.2f}s")

    print("\nExtracting features from Validation set...")
    start_time = time.time()
    X_val_feat_np, y_val_np = extract_features(val_loader, vit_model, image_transform, device)
    print(f"Validation features extracted. Shape: {X_val_feat_np.shape}. Time: {time.time() - start_time:.2f}s")

    print("\nExtracting features from Test set...")
    start_time = time.time()
    X_test_feat_np, y_test_np = extract_features(test_loader, vit_model, image_transform, device)
    print(f"Test features extracted. Shape: {X_test_feat_np.shape}. Time: {time.time() - start_time:.2f}s")

    return (X_train_feat_np, y_train_np), (X_val_feat_np, y_val_np), (X_test_feat_np, y_test_np)

def plot_tsne(X_test_np, y_test_np, num_classes, n_samples_tsne=None):
    """Performs t-SNE visualization on test set features."""
    print("\n" + "="*30)
    print("Starting t-SNE Visualization")
    print("="*30)
    features_to_visualize = X_test_np
    labels_to_visualize = y_test_np
    n_samples_tsne = len(features_to_visualize) if n_samples_tsne is None else min(n_samples_tsne, len(features_to_visualize))
    if n_samples_tsne < len(features_to_visualize):
        print(f"Using a subset of {n_samples_tsne} samples for t-SNE visualization.")
        indices = np.random.choice(len(features_to_visualize), n_samples_tsne, replace=False)
        features_subset = features_to_visualize[indices]
        labels_subset = labels_to_visualize[indices]
    else:
        print(f"Using all {n_samples_tsne} test samples for t-SNE visualization.")
        features_subset = features_to_visualize
        labels_subset = labels_to_visualize
    print("\nApplying t-SNE... (This might take a while depending on data size)")
    start_time = time.time()
    tsne = TSNE(n_components=2, perplexity=30, learning_rate='auto', init='pca', n_iter=1000, random_state=42, verbose=1)
    tsne_results = tsne.fit_transform(features_subset)
    print(f"\nt-SNE finished. Time taken: {time.time() - start_time:.2f} seconds")
    print(f"Shape of t-SNE results: {tsne_results.shape}")
    print("\nGenerating t-SNE plot...")
    plt.figure(figsize=(12, 10))
    unique_labels = np.unique(labels_subset)
    colors = plt.cm.get_cmap('tab10', num_classes)
    for i, label in enumerate(unique_labels):
        idx = np.where(labels_subset == label)
        plt.scatter(tsne_results[idx, 0], tsne_results[idx, 1], color=colors(i), label=f'Class {label}', alpha=0.7, s=50)
    plt.title(f't-SNE Visualization of ViT Features ({n_samples_tsne} Test Samples)')
    plt.xlabel('t-SNE Dimension 1')
    plt.ylabel('t-SNE Dimension 2')
    plt.legend(loc='best', markerscale=1.5, title="Blood Cell Type")
    plt.grid(True, linestyle='--', alpha=0.5)
    plt.tight_layout()
    plt.show()
    print("\nt-SNE visualization complete.")

Feature Processing:

• convert_to_tensors(features_labels) Converts NumPy feature and label arrays to PyTorch tensors for model training.

• scale_features(X_train, X_val, X_test) Scales features using the mean and standard deviation of the training set to standardize train, validation, and test features.

• create_lr_dataloaders(X_train_scaled, y_train, X_val_scaled, y_val, X_test_scaled, y_test, batch_size) Creates PyTorch DataLoaders for logistic regression training from scaled features and labels for train, validation, and test sets.

# Feature Processing
def convert_to_tensors(features_labels):
    """Converts numpy features and labels to PyTorch tensors."""
    X_np, y_np = features_labels
    X_tensor = torch.tensor(X_np, dtype=torch.float32)
    y_tensor = torch.tensor(y_np, dtype=torch.long)
    return X_tensor, y_tensor

def scale_features(X_train, X_val, X_test):
    """Scales features using training set mean and std."""
    print("\nScaling features (using PyTorch)...")
    mean = X_train.mean(dim=0, keepdim=True)
    std = X_train.std(dim=0, keepdim=True)
    std[std == 0] = 1e-6  # Avoid division by zero
    X_train_scaled = (X_train - mean) / std
    X_val_scaled = (X_val - mean) / std
    X_test_scaled = (X_test - mean) / std
    print("Features scaled.")
    return X_train_scaled, X_val_scaled, X_test_scaled

def create_lr_dataloaders(X_train_scaled, y_train, X_val_scaled, y_val, X_test_scaled, y_test, batch_size):
    """Creates DataLoaders for logistic regression training."""
    print("\nCreating DataLoaders for Logistic Regression training...")
    train_dataset_lr = TensorDataset(X_train_scaled, y_train)
    val_dataset_lr = TensorDataset(X_val_scaled, y_val)
    test_dataset_lr = TensorDataset(X_test_scaled, y_test)
    train_loader_lr = DataLoader(train_dataset_lr, batch_size=batch_size, shuffle=True)
    val_loader_lr = DataLoader(val_dataset_lr, batch_size=batch_size, shuffle=False)
    test_loader_lr = DataLoader(test_dataset_lr, batch_size=batch_size, shuffle=False)
    print("DataLoaders created.")
    return train_loader_lr, val_loader_lr, test_loader_lr

Logistic Regression Model

• LogisticRegressionModel(nn.Module) Defines a simple PyTorch logistic regression model with a single linear layer for classification.

• setup_lr_model(feature_dim, num_classes, device, learning_rate) Initializes a logistic regression model, cross-entropy loss, and Adam optimizer, moving the model to the specified device.

# Logistic Regression Model
class LogisticRegressionModel(nn.Module):
    """PyTorch logistic regression model."""
    def __init__(self, input_dim, num_classes):
        super(LogisticRegressionModel, self).__init__()
        self.linear = nn.Linear(input_dim, num_classes)

    def forward(self, x):
        return self.linear(x)

def setup_lr_model(feature_dim, num_classes, device, learning_rate):
    """Initializes logistic regression model, loss, and optimizer."""
    print("\nInitializing PyTorch Logistic Regression model...")
    model = LogisticRegressionModel(feature_dim, num_classes).to(device)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=learning_rate)
    print("Model, Loss, and Optimizer initialized.")
    return model, criterion, optimizer

Training and Evaluation:

• train_lr_model(model, criterion, optimizer, train_loader, val_loader, device, num_epochs) Trains the logistic regression model for a specified number of epochs, tracking training loss and accuracy, and evaluating on the validation set.

• evaluate_model(loader, model, device) Evaluates the model on a DataLoader, returning accuracy, true labels, predicted labels, and class probabilities.

• print_evaluation_results(train_loader, val_loader, test_loader, model, device, num_classes) Evaluates the logistic regression model on train, validation, and test sets, printing accuracy and a classification report for the test set.

# Training and Evaluation
def train_lr_model(model, criterion, optimizer, train_loader, val_loader, device, num_epochs):
    """Trains the logistic regression model."""
    print("\nTraining PyTorch Logistic Regression model...")
    start_time = time.time()
    for epoch in range(num_epochs):
        model.train()
        running_loss = 0.0
        correct_train = 0
        total_train = 0
        train_iterator = tqdm(train_loader, desc=f"Epoch {epoch+1}/{num_epochs}", leave=False)
        for inputs, labels in train_iterator:
            inputs, labels = inputs.to(device), labels.to(device)
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            running_loss += loss.item() * inputs.size(0)
            _, predicted = torch.max(outputs.data, 1)
            total_train += labels.size(0)
            correct_train += (predicted == labels).sum().item()
            train_iterator.set_postfix(loss=loss.item())
        epoch_loss = running_loss / len(train_loader.dataset)
        epoch_acc = correct_train / total_train
        val_acc = evaluate_model(val_loader, model, device)[0]
        # print(f"Epoch {epoch+1}/{num_epochs} - Train Loss: {epoch_loss:.4f}, Train Acc: {epoch_acc:.4f}, Val Acc: {val_acc:.4f}")
    print(f"Logistic Regression training finished. Total time: {time.time() - start_time:.2f}s")
    return model

def evaluate_model(loader, model, device):
    """Evaluates the model on a given DataLoader, returning probabilities, labels, and predictions."""
    model.eval()
    all_probs = []
    all_labels = []
    all_preds = []
    with torch.no_grad():
        for inputs, labels in loader:
            inputs, labels = inputs.to(device), labels.to(device)
            outputs = model(inputs)
            probs = torch.softmax(outputs, dim=1)  # Convert logits to probabilities
            _, predicted = torch.max(outputs.data, 1)
            all_probs.extend(probs.cpu().numpy())
            all_labels.extend(labels.cpu().numpy())
            all_preds.extend(predicted.cpu().numpy())
    accuracy = accuracy_score(all_labels, all_preds)
    return accuracy, np.array(all_labels), np.array(all_preds), np.array(all_probs)

def print_evaluation_results(train_loader, val_loader, test_loader, model, device, num_classes):
    """Evaluates and prints results for train, validation, and test sets."""
    print("\nEvaluating final Logistic Regression model...")
    train_accuracy, _, _, _ = evaluate_model(train_loader, model, device)
    val_accuracy, _, _, _ = evaluate_model(val_loader, model, device)
    test_accuracy, y_test_true, y_test_pred, y_test_probs = evaluate_model(test_loader, model, device)
    print(f"\nFinal Accuracy (LR):")
    print(f"  Train: {train_accuracy:.4f}")
    print(f"  Validation: {val_accuracy:.4f}")
    print(f"  Test: {test_accuracy:.4f}")
    print("\nClassification Report (Test Set - LR):")
    # target_names = [f'Class {i}' for i in range(num_classes)]
    # target_names = train_loader.dataset.classes
    print(classification_report(y_test_true, y_test_pred, target_names=target_names, zero_division=0))
    return y_test_true, y_test_pred, y_test_probs, target_names

# Setup configuration
config = get_config()
print_config(config)

# Setup DataLoaders
# train_loader, val_loader, test_loader = setup_dataloaders(config)

Using device: cpu
ViT Batch Size: 32
LR Batch Size: 64
LR Epochs: 50
LR Learning Rate: 0.001

# Setup ViT model and transform
vit_model, feature_dim = setup_vit_model(config)
image_transform = get_vit_transform(models.ViT_B_16_Weights.IMAGENET1K_V1, config['VIT_INPUT_SIZE'])

# Extract features
(X_train_np, y_train_np), (X_val_np, y_val_np), (X_test_np, y_test_np) = extract_all_features(
    train_loader, val_loader, test_loader, vit_model, image_transform, config['DEVICE']
)

Loading pre-trained ViT model...
Downloading: "https://download.pytorch.org/models/vit_b_16-c867db91.pth" to /Users/yehonatankeypur/.cache/torch/hub/checkpoints/vit_b_16-c867db91.pth

100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 330M/330M [00:32<00:00, 10.6MB/s]

ViT model loaded and modified for feature extraction (output dim: 768).

Extracting features from Train set...

Extracting features: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 374/374 [14:25<00:00,  2.31s/it]

Train features extracted. Shape: (11964, 768). Time: 866.31s

Extracting features from Validation set...

Extracting features: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 81/81 [03:15<00:00,  2.41s/it]

Validation features extracted. Shape: (2564, 768). Time: 195.34s

Extracting features from Test set...

Extracting features: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 81/81 [03:08<00:00,  2.32s/it]

Test features extracted. Shape: (2564, 768). Time: 188.34s

Logistic Regression Pipeline

Now, we transform Vision Transformer (ViT)-extracted features into a format ready for logistic regression, train a simple yet effective model, and evaluate its performance. Here's what we'll do:

• Tensor Conversion: Convert NumPy feature and label arrays for train, validation, and test sets into PyTorch tensors for seamless integration with PyTorch's ecosystem.
• Feature Scaling: Standardize features using the training set's mean and standard deviation to ensure consistent scaling across all sets, optimizing model training.
• DataLoader Creation: Build PyTorch DataLoaders to batch and shuffle data efficiently for training and evaluation.
• Model Setup: Initialize a logistic regression model, cross-entropy loss, and Adam optimizer, all configured for the specified feature dimensions and number of classes.
• Training: Train the model over multiple epochs, monitoring loss and accuracy while validating performance.
• Evaluation: Assess the model on train, validation, and test sets, generating accuracy metrics and a detailed classification report for the test set.

# Convert to tensors
X_train, y_train = convert_to_tensors((X_train_np, y_train_np))
X_val, y_val = convert_to_tensors((X_val_np, y_val_np))
X_test, y_test = convert_to_tensors((X_test_np, y_test_np))

# Scale features
X_train_scaled, X_val_scaled, X_test_scaled = scale_features(X_train, X_val, X_test)

# Create DataLoaders for logistic regression
train_loader_lr, val_loader_lr, test_loader_lr = create_lr_dataloaders(
    X_train_scaled, y_train, X_val_scaled, y_val, X_test_scaled, y_test, config['LR_BATCH_SIZE']
)

Scaling features (using PyTorch)...
Features scaled.

Creating DataLoaders for Logistic Regression training...
DataLoaders created.

# Setup and train logistic regression model
lr_model, criterion, optimizer = setup_lr_model(feature_dim, config['NUM_CLASSES'], config['DEVICE'], config['LR_LEARNING_RATE'])
lr_model = train_lr_model(lr_model, criterion, optimizer, train_loader_lr, val_loader_lr, config['DEVICE'], config['LR_EPOCHS'])

Initializing PyTorch Logistic Regression model...
Model, Loss, and Optimizer initialized.

Training PyTorch Logistic Regression model...

Epoch 16/50:   0%|                                                                                                                                                        | 0/187 [00:00<?, ?it/s, loss=0.069]IOPub message rate exceeded.
The Jupyter server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--ServerApp.iopub_msg_rate_limit`.

Current values:
ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)
ServerApp.rate_limit_window=3.0 (secs)

Epoch 43/50:  67%|████████████████████████████████████████████████████████████████████████████████████████████▉                                             | 126/187 [00:00<00:00, 1252.85it/s, loss=0.00254]IOPub message rate exceeded.
The Jupyter server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--ServerApp.iopub_msg_rate_limit`.

Current values:
ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)
ServerApp.rate_limit_window=3.0 (secs)

# Evaluate model
y_test_true, y_test_pred, y_test_probs, target_names = print_evaluation_results(
    train_loader_lr, val_loader_lr, test_loader_lr, lr_model, config['DEVICE'], target_names
)

Evaluating final Logistic Regression model...

Final Accuracy (LR):
  Train: 0.9972
  Validation: 0.9641
  Test: 0.9665

Classification Report (Test Set - LR):
              precision    recall  f1-score   support

    basophil       0.96      0.97      0.96       182
  eosinophil       1.00      0.99      0.99       468
erythroblast       0.96      0.97      0.96       233
          ig       0.93      0.94      0.94       434
  lymphocyte       0.95      0.95      0.95       182
    monocyte       0.95      0.93      0.94       213
  neutrophil       0.96      0.96      0.96       499
    platelet       0.99      1.00      0.99       353

    accuracy                           0.97      2564
   macro avg       0.96      0.96      0.96      2564
weighted avg       0.97      0.97      0.97      2564

Classification Report Analysis

This classification report summarizes the performance of the logistic regression model on the test set, classifying the blood cell types using features extracted from the Vision Transformer (ViT). The model excels across all classes, with near-perfect scores. Monocyte and IG have slightly lower scores (but also high), suggesting minor challenges in distinguishing these classes. The high accuracy and balanced metrics demonstrate the effectiveness of ViT features combined with logistic regression for blood cell classification.

We will expand the discussion and get down to details:

Exceptional Overall Performance:
The model achieves a test accuracy above 95% across all samples, indicating strong generalization to unseen data. Both macro and weighted averages for precision, recall, and F1-score are above 0.96, reflecting consistent and balanced performance across classes, even with varying sample sizes.

Class Imbalance Handling
The dataset has varying support, yet the weighted average matches the macro average, indicating the model handles class imbalance effectively. The logistic regression model, paired with standardized ViT features, mitigates bias toward larger classes like neutrophil.

Feature Quality
The high F1-scores across all classes highlight the effectiveness of ViT-extracted features. These features capture meaningful patterns in blood cell images, enabling a simple logistic regression model to achieve near-state-of-the-art performance without complex architectures.

Understanding Monocyte and Immature Granulocytes (IG) Morphological Similarity
The classification report highlights that monocytes and IG have slightly lower performance compared to other blood cell types, suggesting potential challenges in distinguishing them. This can be attributed to their similar morphology (see image below), which impacts the model's ability to differentiate these classes based on ViT extracted features. For example, a monocyte might be misclassified as an immature granulocyte if its nucleus shape or cytoplasmic granularity closely resembles that of a myelocyte. The results from the confusion matrix (below) support this claim, as it can be seen that the model classified monocytes instead of IG and vice versa.

The large, irregularly shaped nucleus and granular cytoplasm in both monocytes and immature granulocytes create visual similarities in stained blood smear images. These shared features (e.g., nucleus size, shape, and cytoplasmic texture) can lead to overlapping representations in ViT-extracted features. For example, a monocyte might be misclassified as an immature granulocyte if its nucleus shape or cytoplasmic granularity closely resembles that of a myelocyte.

ViT models capture high-level patterns like cell shape, nucleus-cytoplasm ratio, and texture. Since monocytes and IG share these traits, their feature embeddings may be close in the high-dimensional space, making it harder for the logistic regression model to draw a clear decision boundary.

Monocyte & IG Morphology

Monocyte

IG

Another possible explanation is that monocytes have 213 samples and IG has 434, indicating IG is more represented. This imbalance might bias the model toward IG, potentially causing monocytes to be misclassified as iIGg, as hinted by monocytes' lower precision compared to recall.

Despite this, the model still achieves high performance, indicating that ViT features capture enough distinguishing information to separate most instances correctly. The drop in performance is minor, but it highlights a limitation in relying solely on visual morphology for these classes.

Potential Improvements
The high performance suggests the model is well-optimized, but testing on a more diverse or noisy dataset could validate its robustness for real-world applications like medical diagnostics.

Conclusion
The model demonstrates excellent classification capabilities, leveraging ViT features to achieve high accuracy and balanced metrics across all blood cell types. Minor challenges with monocytes and IG offer opportunities for refinement, but the results strongly support the use of ViT with logistic regression for reliable blood cell classification in clinical settings.

plot_confusion_matrix(y_test_true, y_test_pred, target_names)

plot_roc_and_threshold_curves(y_test_true, y_test_probs, config['NUM_CLASSES'], target_names)

Model Analysis

ROC Curve

Overall Performance: The ROC curve plots the True Positive Rate (TPR) against the False Positive Rate (FPR). The curve hugging the top-left corner indicates excellent model performance. A perfect classifier would go straight up the Y-axis to (0,1) and then across to (1,1). The linear model's micro-average curve (dotted orange line) is very close to this ideal shape. AUC (Area Under the Curve): The Micro-Average AUC of 0.999. An AUC of 1 represents a perfect classifier, while 0.5 represents a random classifier (the dashed diagonal line). An AUC of 0.999 is extremely high, suggesting outstanding discriminative ability across all classes combined.

Analysis of TPR and FPR at Every Threshold

Individual Class Performance (AUCs): This plot provides more detail for each of the 8 blood cell types:

• Basophil, Eosinophil, Platelet: Achieved an AUC of 1.000. This indicates perfect or near-perfect separation for these classes based on the test data. The model can distinguish these cell types from others flawlessly according to this metric.
• Erythroblast, Lymphocyte, Neutrophil: Achieved AUCs of 0.999. This represents exceptionally high performance, very close to perfect.
• Monocyte: AUC of 0.996. Still indicates excellent performance.
• IG: Achieved an AUC of 0.995. While slightly lower than the others, this is still a very high AUC value, indicating strong classification performance for this class as well.

TPR and FPR Behavior vs. Threshold:

• High TPR: The solid lines (TPR) for nearly all classes remain close to 1.0 across a wide range of decision thresholds. This means the model correctly identifies a high proportion of true positive cases for each cell type, even when the decision threshold varies.
• Low FPR: The dashed lines (FPR) for all classes are clustered very close to 0 for most threshold values. They only start to rise noticeably at very high thresholds. This signifies that the model makes very few false positive errors; it rarely misclassifies other cell types as the target cell type.

The plot clearly shows the trade-off: as the threshold increases (making the model stricter), the TPR eventually starts to drop (missing some true positives), while the FPR generally stays low until very high thresholds. Conversely, at very low thresholds, FPR might slightly increase for some classes while TPR is maximized. However, for our model, both TPR and FPR are excellent across a broad, practical range of thresholds.

Insights and Conclusions

• Outstanding Model Performance: The combination of features extracted by the Vision Transformer (ViT) and classification using Logistic Regression has resulted in an exceptionally high-performing model for identifying the blood cells on the given test set.
• Highly Discriminative Features: The near-perfect AUC scores strongly suggest that the features learned and extracted by the ViT are highly discriminative and capture the essential differences between the various blood cell types effectively.
• The model maintains high TPR and low FPR across a wide range of decision thresholds. This indicates that the performance is not overly sensitive to the exact threshold chosen for classifying a cell, making it potentially more reliable in practice.
• While all classes were classified with high accuracy, the model showed particularly strong (potentially perfect) separation for Basophils, Eosinophils, and Platelets based on the AUC scores. Even the class with the lowest AUC (IG) demonstrates excellent classification.

Using a powerful feature extractor like ViT followed by a relatively simple linear classifier like Logistic Regression proved to be a very successful strategy for this specific image classification task. In summary, our model demonstrates a highly accurate and robust method for blood cell classification. The performance metrics (AUC, TPR, FPR) are consistently excellent across all classes, validating the effectiveness of our chosen approach (ViT + Logistic Regression).

Decision Tree on ViT Features

Following the extraction of potent visual features using the Vision Transformer (ViT), we now explore an alternative classification strategy. We will apply classification of the blood cell types using a Decision Tree model, applied directly to the ViT-derived feature vectors.

The reason we are investigating a Decision Tree approach is to assess a different type of classification logic compared to linear models like Logistic Regression. Decision Trees operate by creating a hierarchical structure of rules, partitioning the data based on feature values. This offers several potential advantages:

• Interpretability: Decision Trees can often be visualized, allowing us to understand the specific sequence of feature-based decisions the model makes to arrive at a classification.
• Non-Linearity: They can inherently capture non-linear relationships between the features and the class labels without requiring explicit feature transformation.
• Alternative Perspective: Employing a Decision Tree provides insight into whether a rule-based partitioning of the ViT feature space is an effective method for separating these blood cell classes, complementing the insights gained from the previous model.

To implement this, we will start by preparing our dataset, ensuring it is suitable for training a decision tree. We will then split the data into training and testing sets. Next, we will train a decision tree model on the training data and evaluate its performance on the test set using appropriate metrics. We will also visualize the structure of the decision tree to interpret how the model makes its predictions and to identify the most important features influencing the outcome.

Utility Functions

DataLoader Setup

• setup_dataloaders_dummy(config) Creates placeholder DataLoaders for train, validation, and test sets using dummy data, configured with batch size, number of classes, and image dimensions from the provided config.

Feature Scaling:

• scale_features(X_train_np, X_val_np, X_test_np) Scales features using scikit-learn's StandardScaler, fitting on the training set and transforming train, validation, and test sets to standardize them.

Decision Tree Model:

• setup_dt_model(config) Initializes a scikit-learn DecisionTreeClassifier with specified max depth, minimum samples split, and random state from the config.

• train_dt_model(model, X_train, y_train, X_val, y_val) Trains the Decision Tree model on the training data, computes training and validation accuracies, and returns the trained model.

Evaluation:

• evaluate_model_dt(X, y, model) Evaluates the Decision Tree model on given features and labels, returning accuracy, true labels, predicted labels, and class probabilities.

• print_evaluation_results_dt(X_train, y_train, X_val, y_val, X_test, y_test, model, num_classes) Evaluates the Decision Tree model on train, validation, and test sets, printing accuracies and a classification report for the test set.

from sklearn.tree import DecisionTreeClassifier

def setup_dataloaders_dummy(config):
    """Sets up placeholder DataLoaders for train, validation, and test sets."""
    print("Creating placeholder DataLoaders...")
    train_loader = create_dummy_loader(500, config['VIT_BATCH_SIZE'], config['NUM_CLASSES'], config['IMG_HEIGHT'], config['IMG_WIDTH'])
    val_loader = create_dummy_loader(100, config['VIT_BATCH_SIZE'], config['NUM_CLASSES'], config['IMG_HEIGHT'], config['IMG_WIDTH'])
    test_loader = create_dummy_loader(100, config['VIT_BATCH_SIZE'], config['NUM_CLASSES'], config['IMG_HEIGHT'], config['IMG_WIDTH'])
    print("Placeholder DataLoaders created.")
    return train_loader, val_loader, test_loader

# Scales features using training set mean and std with scikit-learn
def scale_features(X_train_np, X_val_np, X_test_np):
    print("\nScaling features...")
    scaler = StandardScaler()
    X_train_scaled = scaler.fit_transform(X_train_np)
    X_val_scaled = scaler.transform(X_val_np)
    X_test_scaled = scaler.transform(X_test_np)
    print("Features scaled.")
    return X_train_scaled, X_val_scaled, X_test_scaled

# Decision Tree Model
def setup_dt_model(config):
    print("\nInitializing Decision Tree model...")
    model = DecisionTreeClassifier(
        max_depth=config['DT_MAX_DEPTH'],
        min_samples_split=config['DT_MIN_SAMPLES_SPLIT'],
        random_state=config['DT_RANDOM_STATE']
    )
    print("Decision Tree model initialized.")
    return model

def train_dt_model(model, X_train, y_train, X_val, y_val):
    print("\nTraining Decision Tree model...")
    start_time = time.time()
    model.fit(X_train, y_train)
    train_accuracy = model.score(X_train, y_train)
    val_accuracy = model.score(X_val, y_val)
    print(f"Decision Tree training finished. Total time: {time.time() - start_time:.2f}s")
    print(f"Train Accuracy: {train_accuracy:.4f}")
    print(f"Validation Accuracy: {val_accuracy:.4f}")
    return model

# Evaluation
def evaluate_model_dt(X, y, model):
    y_pred = model.predict(X)
    y_probs = model.predict_proba(X)
    accuracy = accuracy_score(y, y_pred)
    return accuracy, y, y_pred, y_probs

def print_evaluation_results_dt(X_train, y_train, X_val, y_val, X_test, y_test, model, num_classes):
    """Evaluates and prints results for train, validation, and test sets."""
    print("\nEvaluating Decision Tree model...")
    train_accuracy, _, _, _ = evaluate_model_dt(X_train, y_train, model)
    val_accuracy, _, _, _ = evaluate_model_dt(X_val, y_val, model)
    test_accuracy, y_test_true, y_test_pred, y_test_probs = evaluate_model_dt(X_test, y_test, model)
    print(f"\nFinal Accuracy (Decision Tree):")
    print(f"  Train: {train_accuracy:.4f}")
    print(f"  Validation: {val_accuracy:.4f}")
    print(f"  Test: {test_accuracy:.4f}")
    print("\nClassification Report (Test Set - Decision Tree):")
    print(classification_report(y_test_true, y_test_pred, target_names=target_names, zero_division=0))
    return y_test_true, y_test_pred, y_test_probs

Configuration

# Setup ViT model and transform
vit_model, feature_dim = setup_vit_model(config)
image_transform = get_vit_transform(models.ViT_B_16_Weights.IMAGENET1K_V1, config['VIT_INPUT_SIZE'])

Loading pre-trained ViT model...
ViT model loaded and modified for feature extraction (output dim: 768).

Decision Tree Pipeline

# Extract features
(X_train_np, y_train_np), (X_val_np, y_val_np), (X_test_np, y_test_np) = extract_all_features(
    train_loader, val_loader, test_loader, vit_model, image_transform, config['DEVICE']
)

Extracting features from Train set...

Extracting features: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 374/374 [14:37<00:00,  2.35s/it]

Train features extracted. Shape: (11964, 768). Time: 877.97s

Extracting features from Validation set...

Extracting features: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 81/81 [03:04<00:00,  2.27s/it]

Validation features extracted. Shape: (2564, 768). Time: 184.31s

Extracting features from Test set...

Extracting features: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 81/81 [03:09<00:00,  2.34s/it]

Test features extracted. Shape: (2564, 768). Time: 189.84s

# Scale features
X_train_scaled, X_val_scaled, X_test_scaled = scale_features(X_train_np, X_val_np, X_test_np)

Scaling features...
Features scaled.

# Setup and train decision tree model
dt_model = setup_dt_model(config)
dt_model = train_dt_model(dt_model, X_train_scaled, y_train_np, X_val_scaled, y_val_np)

Initializing Decision Tree model...
Decision Tree model initialized.

Training Decision Tree model...
Decision Tree training finished. Total time: 6.04s
Train Accuracy: 0.8353
Validation Accuracy: 0.6486

# Evaluate model
y_test_true, y_test_pred, y_test_probs = print_evaluation_results_dt(
    X_train_scaled, y_train_np, X_val_scaled, y_val_np, X_test_scaled, y_test_np, dt_model, config['NUM_CLASSES']
)

Evaluating Decision Tree model...

Final Accuracy (Decision Tree):
  Train: 0.8353
  Validation: 0.6486
  Test: 0.6408

Classification Report (Test Set - Decision Tree):
              precision    recall  f1-score   support

    basophil       0.39      0.40      0.39       182
  eosinophil       0.76      0.76      0.76       468
erythroblast       0.74      0.63      0.68       233
          ig       0.54      0.63      0.58       434
  lymphocyte       0.46      0.45      0.46       182
    monocyte       0.41      0.36      0.38       213
  neutrophil       0.68      0.69      0.69       499
    platelet       0.86      0.83      0.85       353

    accuracy                           0.64      2564
   macro avg       0.61      0.59      0.60      2564
weighted avg       0.64      0.64      0.64      2564

plot_confusion_matrix(y_test_true, y_test_pred, target_names)

Model Analysis

Accuracy Scores

• Train Accuracy: The model learned to classify the training data with roughly 83.7% accuracy. This shows it was able to find patterns within the training set.
• Validation Accuracy and Test Accuracy: These scores are significantly lower than the training accuracy. The test accuracy, which represents the model's performance on completely unseen data, is about 65.8%.

Train vs. Test Gap:
The large drop in accuracy from training to testing is a classic sign of overfitting. The Decision Tree model has learned the training data too specifically, including its noise and idiosyncrasies, and therefore doesn't generalize well to new, unseen data.

Classification Report

The overall accuracy confirms the moderate performance on the test set. The performance is not uniform across the 8 blood cell types:

• Best Performing: platelet and eosinophil are classified reasonably well, with good balance of precision and recall.
• Moderately Performing: neutrophil, erythroblast, and IG show moderate results. IG notably has higher recall but lower precision, meaning it finds a decent portion of 'ig' cells but often misclassifies other types as 'ig'.
• Poorly Performing: lymphocyte, basophil, and monocyte are classified poorly. Monocyte and lymphocyte suffer particularly from low recall, meaning the model misses identifying many of these actual cells. basophil and monocyte also have very low precision, indicating that when the model predicts these types, it's incorrect more often than it's correct.

Averages:

• Macro Avg F1: The average F1 score across classes (treating all classes equally) is dragged down by the poorly performing classes.
• Weighted Avg F1: The average F1 score weighted by the number of samples per class is close to the overall accuracy, reflecting the performance distribution across classes with varying support levels. Insights and Conclusions:

The Decision Tree classifier, using ViT features, achieves only moderate success on the test for the classification problem. The model suffers from substantial overfitting, as evidenced by the large gap between training and testing accuracy. This is a common characteristic of unpruned or overly complex decision trees. The model's ability to classify different cell types varies significantly. It handles platelets and eosinophils adequately but struggles considerably with monocytes, basophils, and lymphocytes.

Comparing to Logistic Regression
Comparing these results to the previously implied near-perfect performance of the Logistic Regression model, the Decision Tree performs substantially worse on the same ViT features. This suggests that the linear separation found by Logistic Regression might be more effective for these specific features than the hierarchical, axis-aligned splits created by the Decision Tree.

In conclusion, while the Decision Tree provides some classification capability, it is significantly hampered by overfitting and performs inconsistently across classes in this specific application with ViT features. It appears less suitable than the Logistic Regression approach for effectively leveraging these features, unless significant hyperparameter tuning or transitioning to ensemble tree methods is undertaken.

 plot_roc_and_threshold_curves(y_test_true, y_test_probs, config['NUM_CLASSES'], target_names)

Insights and Conclusions

ROC Curve Plot

• General Shape: Unlike the near-perfect curves from the Logistic Regression model, these ROC curves are noticeably further away from the ideal top-left corner. They still demonstrate performance better than random guessing (the dashed diagonal line), but indicate significantly less discriminative power.
• Micro-Average Curve: The orange dotted line represents the micro-average performance across all classes. Its bow towards the diagonal confirms the moderate overall performance level.

TPR and FPR at Every Threshold

AUC Scores:
This plot provides the specific Area Under the Curve (AUC) values, which quantify the model's ability to distinguish between positive and negative classes: The Micro-Average AUC and Individual Class AUCs results confirms moderate overall discriminative ability across all classes combined.

TPR/FPR Trade-off:
The plot shows that for many classes, the True Positive Rate (TPR) starts to decrease significantly even at relatively low decision thresholds if you want to keep the False Positive Rate (FPR) low. Compared to the previous Logistic Regression plots, where TPR stayed high and FPR stayed low across a wide threshold range, the trade-off here is much less favorable. Achieving high TPR often requires accepting a higher FPR, and vice-versa.

The variability is clear: platelet maintains a higher TPR for longer, while basophil, monocyte, and lymphocyte show sharp drops in TPR or quicker rises in FPR, aligning with their lower AUC scores.

Insights and Conclusions:
The ROC curves and AUC values quantitatively confirm the findings from the classification report. An overall Micro-Average AUC that indicates moderate, not excellent, classification capability. The Decision Tree model, when applied to the ViT features, demonstrates significantly less ability to discriminate between the different blood cell classes compared to the Logistic Regression model.

The ranking of classes by AUC score is consistent with the F1-scores seen in the classification report, reinforcing which classes the model handles better or worse.

While ROC/AUC measures performance on the test set, the substantially lower AUCs compared to the near-perfect ones from the Logistic Regression model (using the same features) further suggest that the Decision Tree struggled to generalize the patterns learned during training. The complex rules it created likely didn't translate well to unseen data, resulting in poorer discrimination.

Neural Networks

Neural Networks Utility Functions

Train and Test Functions

• train_model(...) Trains a model for a specified number of epochs with training and validation data, while tracking performance metrics (loss, accuracy, precision, recall, F1). Supports early stopping, which halts training if the monitored metric doesn't improve for a set number of epochs.

• train_model_without_stop(...) Performs model training and validation similarly to train_model, but without early stopping. Tracks and logs key performance metrics across all epochs.

• test_model(...) Evaluates a trained model on a test dataset, computes performance metrics, and stores results including the confusion matrix. Intended for final assessment after training is complete.

def train_model(epochs, train_loader, val_loader, model, loss_function,
                optimizer, accuracy_metric, device, num_classes,
                early_stopping_patience=None,
                early_stopping_metric='val_loss',
                early_stopping_min_delta=0.001, # Minimal change that would be considered an improvement
                debug=False, print_progress=False):
    """Train and validate the model, tracking metrics and implementing early stopping."""
    precision_metric = MulticlassPrecision(num_classes=num_classes, average="weighted", zero_division=0).to(device)
    recall_metric = MulticlassRecall(num_classes=num_classes, average="weighted", zero_division=0).to(device)
    f1_score_metric = MulticlassF1Score(num_classes=num_classes, average="weighted", zero_division=0).to(device)

    if not hasattr(model, 'history'):
        model.history = {
            "train_loss": [], "train_acc": [], "train_precision": [],
            "val_loss": [], "val_acc": [], "val_precision": [],
            "val_recall": [], "val_f1_score": []
        }

    # Early Stopping
    epochs_no_improve = 0
    best_metric_value = None
    best_model_state = None
    early_stopping_active = early_stopping_patience is not None

    # Determining the optimization mode
    if early_stopping_active:
        if early_stopping_metric in ['val_acc', 'val_precision', 'val_recall', 'val_f1_score']:
            early_stopping_mode = 'max'
            best_metric_value = -np.inf # Lowest value
        elif early_stopping_metric == 'val_loss':
            early_stopping_mode = 'min'
            best_metric_value = np.inf # Highest value
        else:
            raise ValueError(f"Unknown early_stopping_metric: {early_stopping_metric}")
        print(f"Early stopping enabled: Monitoring '{early_stopping_metric}', Patience={early_stopping_patience}, Mode='{early_stopping_mode}', Min Delta={early_stopping_min_delta}")

    for epoch in trange(epochs, desc="Overall Progress: Epochs", leave=True,
                        position=0, bar_format="{l_bar}{bar} | Batch {n_fmt}/{total_fmt}"):

        train_loss, train_acc, train_precision = 0, 0, 0
        accuracy_metric.reset()
        precision_metric.reset()

        model.train()
        for batch, (X, y) in enumerate(tqdm(train_loader, total=len(train_loader), desc=f"Epoch {epoch + 1}: Training Phase",
                                            leave=False, position=1, bar_format="{l_bar}{bar} | Batch {n_fmt}/{total_fmt}")):
            X, y = X.to(device), y.to(device)
            y_pred = model(X)
            loss = loss_function(y_pred, y)
            train_loss += loss.item()
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            train_acc += accuracy_metric(y_pred, y).item()
            train_precision += precision_metric(y_pred, y).item()

            if debug and batch == 0:
                print("\nDebug Info (First Batch):")
                print(f"  X shape: {X.shape}, y shape: {y.shape}, y unique: {torch.unique(y)}")
                # check_model_numerics(model, X)
                print("-" * 50)

        train_loss /= len(train_loader)
        train_acc /= len(train_loader)
        train_precision /= len(train_loader)

        model.history["train_loss"].append(train_loss)
        model.history["train_acc"].append(train_acc)
        model.history["train_precision"].append(train_precision)

        model.eval()
        val_loss, val_acc, val_precision, val_recall, val_f1 = 0, 0, 0, 0, 0
        precision_metric.reset()
        recall_metric.reset()
        f1_score_metric.reset()
        accuracy_metric.reset()

        with torch.inference_mode():
            for X, y in tqdm(val_loader, total=len(val_loader), desc=f"Epoch {epoch + 1}: Validation Phase",
                             leave=False, position=2, bar_format="{l_bar}{bar} | Batch {n_fmt}/{total_fmt}"):
                X, y = X.to(device), y.to(device)
                val_pred = model(X)
                val_loss += loss_function(val_pred, y).item()

                val_acc += accuracy_metric(val_pred, y).item()
                val_precision += precision_metric(val_pred, y).item()
                val_recall += recall_metric(val_pred, y).item()
                val_f1 += f1_score_metric(val_pred, y).item()

            val_loss /= len(val_loader)
            val_acc /= len(val_loader)
            val_precision /= len(val_loader)
            val_recall /= len(val_loader)
            val_f1 /= len(val_loader)

            model.history["val_loss"].append(val_loss)
            model.history["val_acc"].append(val_acc)
            model.history["val_precision"].append(val_precision)
            model.history["val_recall"].append(val_recall)
            model.history["val_f1_score"].append(val_f1)

        if print_progress or (debug and epoch % 10 == 0):
            print(f"\nEpoch {epoch + 1}/{epochs} Performance Report:")
            print(f"└─ [Train] Loss: {train_loss:.4f} | Accuracy: {train_acc:.2f} | Precision: {train_precision:.2f}")
            print(f"└─ [Validation] Loss: {val_loss:.4f} | Accuracy: {val_acc:.2f} | Precision: {val_precision:.2f} | Recall: {val_recall:.2f} | F1-Score: {val_f1:.2f}")

        # Early Stopping
        if early_stopping_active:
            current_metric_value = model.history[early_stopping_metric][-1] # Take the last value

            # Check improvement
            improved = False
            if early_stopping_mode == 'min':
                if current_metric_value < best_metric_value - early_stopping_min_delta:
                    improved = True
            else: # mode == 'max'
                if current_metric_value > best_metric_value + early_stopping_min_delta:
                    improved = True

            if improved:
                best_metric_value = current_metric_value
                epochs_no_improve = 0
                # Save the best model
                best_model_state = copy.deepcopy(model.state_dict())
                # print(f"Epoch {epoch + 1}: {early_stopping_metric} improved to {best_metric_value:.4f}. Resetting counter.")
            else:
                epochs_no_improve += 1
                print(f"Epoch {epoch + 1}: No improvement in {early_stopping_metric} for {epochs_no_improve} epoch(s).")

            # Check for stopping
            if epochs_no_improve >= early_stopping_patience:
                print(f"\nEarly stopping triggered after {epoch + 1} epochs.")
                print(f"Monitored metric '{early_stopping_metric}' did not improve for {early_stopping_patience} epochs.")

                if best_model_state is not None:
                    print("Loading best model weights found during training.")
                    model.load_state_dict(best_model_state)
                break # Stop the epochs

    print("Finished training loop.")
    if early_stopping_active and best_model_state is None and epoch == epochs - 1:
         print("Warning: Early stopping was active but never triggered improvement or saving.")

    return model.history

def train_model_without_stop(epochs, train_loader, val_loader, model, loss_function,
                optimizer, accuracy_metric, device, num_classes, debug=False, print_progress=False):
    """Train and validate the model, tracking metrics for both sets."""
    precision_metric = MulticlassPrecision(num_classes=num_classes, average="weighted", zero_division=0).to(device)
    recall_metric = MulticlassRecall(num_classes=num_classes, average="weighted", zero_division=0).to(device)
    f1_score_metric = MulticlassF1Score(num_classes=num_classes, average="weighted", zero_division=0).to(device)

    if not hasattr(model, 'history'):
        model.history = {
            "train_loss": [],
            "train_acc": [],
            "train_precision": [],
            "val_loss": [],
            "val_acc": [],
            "val_precision": [],
            "val_recall": [],
            "val_f1_score": []
        }

    for epoch in trange(epochs, desc="Overall Progress: Epochs", leave=True,
                        position=0, bar_format="{l_bar}{bar} | Batch {n_fmt}/{total_fmt}"):
        train_loss, train_acc, train_precision = 0, 0, 0
        accuracy_metric.reset()
        precision_metric.reset()

        model.train()
        for batch, (X, y) in enumerate(tqdm(train_loader, total=len(train_loader), desc=f"Epoch {epoch + 1}: Training Phase",
                                            leave=False, position=1, bar_format="{l_bar}{bar} | Batch {n_fmt}/{total_fmt}")):
            X, y = X.to(device), y.to(device)
            y_pred = model(X)
            loss = loss_function(y_pred, y)
            train_loss += loss.item()
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            train_acc += accuracy_metric(y_pred, y).item()
            train_precision += precision_metric(y_pred, y).item()

            if debug:
                print("Check model numeric (debug):")
                print("X shape:", X.shape)
                print("y shape:", y.shape)
                print("y unique values:", torch.unique(y))
                print(f"[batch={batch}]")
                check_model_numerics(model, X)
                print(f"Looked at {batch * len(X)}/{len(train_loader.dataset)} samples")
                print("-" * 50)

        train_loss /= len(train_loader)
        train_acc /= len(train_loader)
        train_precision /= len(train_loader)

        model.history["train_loss"].append(train_loss)
        model.history["train_acc"].append(train_acc)
        model.history["train_precision"].append(train_precision)

        model.eval()
        val_loss, val_acc, val_precision, val_recall, val_f1 = 0, 0, 0, 0, 0
        precision_metric.reset()
        recall_metric.reset()
        f1_score_metric.reset()
        accuracy_metric.reset()

        with torch.inference_mode():
            for X, y in tqdm(val_loader, total=len(val_loader), desc=f"Epoch {epoch + 1}: Validation Phase",
                             leave=False, position=2, bar_format="{l_bar}{bar} | Batch {n_fmt}/{total_fmt}"):
                X, y = X.to(device), y.to(device)
                val_pred = model(X)
                val_loss += loss_function(val_pred, y).item()

                val_acc += accuracy_metric(val_pred, y).item()
                val_precision += precision_metric(val_pred, y).item()
                val_recall += recall_metric(val_pred, y).item()
                val_f1 += f1_score_metric(val_pred, y).item()

            val_loss /= len(val_loader)
            val_acc /= len(val_loader)
            val_precision /= len(val_loader)
            val_recall /= len(val_loader)
            val_f1 /= len(val_loader)

            model.history["val_loss"].append(val_loss)
            model.history["val_acc"].append(val_acc)
            model.history["val_precision"].append(val_precision)
            model.history["val_recall"].append(val_recall)
            model.history["val_f1_score"].append(val_f1)

        if epoch == 10:
            print(f"\nEpoch {epoch + 1}/{epochs} Performance Report:")
            print(f"└─ [Train] Loss: {train_loss:.4f} | Accuracy: {train_acc:.2f} | Precision: {train_precision:.2f}")
            print(f"└─ [Validation] Loss: {val_loss:.4f} | Accuracy: {val_acc:.2f} | Precision: {val_precision:.2f} | Recall: {val_recall:.2f} | F1-Score: {val_f1:.2f}")
    print("Finished training and validation.")
    return model.history

def test_model(test_loader, model, loss_function, accuracy_metric, device, num_classes, print_progress=True):
    """Evaluate the model on the test set."""
    precision_metric = MulticlassPrecision(num_classes=num_classes, average="weighted", zero_division=0).to(device)
    recall_metric = MulticlassRecall(num_classes=num_classes, average="weighted", zero_division=0).to(device)
    f1_score_metric = MulticlassF1Score(num_classes=num_classes, average="weighted", zero_division=0).to(device)
    confusion_matrix_metric = ConfusionMatrix(num_classes=num_classes, task="multiclass").to(device)

    if not hasattr(model, 'history'):
      model.history = {
          "test_loss": [],
          "test_acc": [],
          "test_precision": [],
          "test_recall": [],
          "test_f1_score": [],
          "confusion_matrix": []
          }

    test_loss, test_acc, test_precision, test_recall, test_f1 = 0, 0, 0, 0, 0
    y_true, y_pred = [], []

    model.eval()
    accuracy_metric.reset()
    precision_metric.reset()
    recall_metric.reset()
    f1_score_metric.reset()
    confusion_matrix_metric.reset()

    with torch.inference_mode():
        for X, y in test_loader:
            X, y = X.to(device), y.to(device)
            test_pred = model(X)
            test_loss += loss_function(test_pred, y).item()

            test_acc += accuracy_metric(test_pred, y).item()
            test_precision += precision_metric(test_pred, y).item()
            test_recall += recall_metric(test_pred, y).item()
            test_f1 += f1_score_metric(test_pred, y).item()
            confusion_matrix_metric.update(test_pred, y)
            y_true.extend(y.cpu().numpy())
            y_pred.extend(test_pred.argmax(dim=1).cpu().numpy())

        test_loss /= len(test_loader)
        test_acc /= len(test_loader)
        test_precision /= len(test_loader)
        test_recall /= len(test_loader)
        test_f1 /= len(test_loader)

    model.history["test_loss"].append(test_loss)
    model.history["test_acc"].append(test_acc)
    model.history["test_precision"].append(test_precision)
    model.history["test_recall"].append(test_recall)
    model.history["test_f1_score"].append(test_f1)
    model.history["confusion_matrix"].append(confusion_matrix_metric.compute().cpu().numpy())
    confusion_matrix_metric.reset()

    if print_progress == True:
        print(f"\n[Test] Loss: {test_loss:.4f} | Accuracy: {test_acc:.2f} | Precision: {test_precision:.2f} | Recall: {test_recall:.2f} | F1-Score: {test_f1:.2f}")

    print("Finished test evaluation.")

Model Analysis Functions (SHAP and Model Performance)

Understanding SHAP plot:
SHAP's DeepExplainer returns SHAP values for each pixel or region in the image, showing how each part of the image contributed to the model's decision.

The Color Meaning:

• Red - regions that contribute to increasing the likelihood of the prediction (this region helps the model identify what it predicted).
• Blue - regions that contribute to decreasing the likelihood of the prediction (this region opposes what the model predicted).
• White or gray - neutral regions (have almost no effect).

From the doc for shap.DeepExplainer:

"...we approximate the conditional expectations of SHAP values using a selection of background samples. Lundberg and Lee, NIPS 2017 showed that the per node attribution rules in DeepLIFT (Shrikumar, Greenside, and Kundaje, arXiv 2017) can be chosen to approximate Shapley values. By integrating over many background samples, Deep estimates approximate SHAP values such that they sum up to the difference between the expected model output on the passed background samples and the current model output $(f(x) - E[f(x)])$."

See also shap.PartitionExplainer docs.

---

Functions:

• visualize_shap_values() - Generates and displays SHAP (SHapley Additive exPlanations) values for a small set of test images using a PyTorch model. It uses a subset of the test data as a background reference and shows how input features contribute to the model's predictions.

• visualize_shap_values_cuda - Similar to visualize_shap_values, this function also computes SHAP values for test images using a model on CUDA.

• shap_partition() - Applies SHAP's PartitionExplainer to visualize which parts of the input images influenced model predictions. This function uses an inpainting masker to hide parts of the image and shows explanations across multiple classes with labeled outputs.

• plot_model_performance() - Creates a 2x2 visual summary of model training and evaluation metrics, including loss, accuracy, precision, recall, F1-score, and a confusion matrix.

def visualize_shap_values(model, test_loader, background_size=20, test_size=3, transpose_order=(4, 0, 2, 3, 1)):
    """
    Generate and visualize SHAP values for a sample of test images.

    Parameters:
    - model: Trained PyTorch model (on CUDA)
    - test_loader: PyTorch DataLoader containing test data
    - background_size: Number of images to use as background (default: 20)
    - test_size: Number of test images to explain (default: 3)
    - transpose_order: Order for transposing SHAP values (default: (4, 0, 2, 3, 1))
    """
    # Ensure model is in evaluation mode and on device
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    model.eval()

    # Get a random batch from test_loader
    batch = next(iter(test_loader))
    images, _ = batch

    # Move images to CUDA
    images = images.to(device)

    # Select background and test images
    background = images[:background_size]
    test_images = images[background_size:background_size + test_size]

    background = background.to(device)
    test_images = test_images.to(device)

    # Compute SHAP values
    explainer = shap.DeepExplainer(model, background)
    shap_values = explainer.shap_values(test_images)

    # Convert to numpy and adjust axes
    shap_numpy = list(np.transpose(shap_values, transpose_order))
    test_numpy = np.swapaxes(np.swapaxes(test_images.cpu().numpy(), 1, -1), 1, 2)

    # Plot the feature attributions
    shap.image_plot(shap_numpy, test_numpy)

def visualize_shap_values_cuda(model, test_loader, background_size=20, test_size=3, transpose_order=(4, 0, 2, 3, 1)):
    """
    Generate and visualize SHAP values for a sample of test images.

    Parameters:
    - model: Trained PyTorch model (on CUDA)
    - test_loader: PyTorch DataLoader containing test data
    - background_size: Number of images to use as background (default: 20)
    - test_size: Number of test images to explain (default: 3)
    - transpose_order: Order for transposing SHAP values (default: (4, 0, 2, 3, 1))
    """
    # Ensure model is in evaluation mode and on CUDA
    model.eval()
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    # Get a random batch from test_loader
    batch = next(iter(test_loader))
    images, _ = batch

    # Move images to CUDA
    images = images.to(device)

    # Select background and test images
    background = images[:background_size]
    test_images = images[background_size:background_size + test_size]

    # Compute SHAP values
    explainer = shap.DeepExplainer(model, background)
    shap_values = explainer.shap_values(test_images)

    # Convert to numpy and adjust axes
    shap_numpy = list(np.transpose(shap_values, transpose_order))
    test_numpy = np.swapaxes(np.swapaxes(test_images.cpu().numpy(), 1, -1), 1, 2)

    # Plot the feature attributions
    shap.image_plot(shap_numpy, test_numpy)

def shap_partition(model, test_dataset, device, num_samples=10, class_names=None):
    """
    Compute and visualize SHAP explanations for a model's predictions on a test dataset.

    Args:
        model: PyTorch model
        test_dataset: PyTorch dataset containing test data
        device: PyTorch device (e.g., 'cuda' or 'cpu')
        num_samples: Number of test samples to analyze (default: 10)
        class_names: List of class names (optional, defaults to dataset.classes)
    """

    # Set model to evaluation mode and move to device
    model.eval()
    model.to(device)

    # Select subset of test data
    test_subset = torch.utils.data.Subset(test_dataset, indices=range(num_samples))
    test_subset_loader = torch.utils.data.DataLoader(test_subset, batch_size=num_samples, shuffle=False)
    X_test, y_test = next(iter(test_subset_loader))
    X_test_np = X_test.cpu().numpy()

    # Convert to uint8 format
    def convert_to_uint8(images):
        images = np.clip(images, 0, 1)
        images = (images * 255).astype(np.uint8)
        images = images.transpose(0, 2, 3, 1)  # (batch, channels, height, width) to (batch, height, width, channels)
        return images

    X_test_uint8 = convert_to_uint8(X_test_np)
    # print("X_test_uint8 shape:", X_test_uint8.shape)
    # print("X_test_uint8 dtype:", X_test_uint8.dtype)

    # Model wrapper for SHAP
    def model_predict(inputs):
        inputs = inputs.transpose(0, 3, 1, 2)  # Reshape to (batch, channels, height, width)
        inputs = inputs.astype(np.float32) / 255.0  # Convert to float32 and scale to [0, 1]
        inputs = torch.tensor(inputs, dtype=torch.float32).to(device)
        with torch.no_grad():
            outputs = model(inputs)
        return torch.softmax(outputs, dim=1).cpu().numpy()

    # Initialize masker and explainer
    masker = shap.maskers.Image("inpaint_telea", X_test_uint8[0].shape)
    # masker = shap.maskers.Image("blur(128,128)", X_test_uint8[0].shape)
    explainer = shap.PartitionExplainer(model_predict, masker)

    # Compute SHAP values
    shap_values = explainer(X_test_uint8, outputs=np.arange(8))

    # Use provided class names or get from dataset
    if class_names is None:
        class_names = test_dataset.classes

    # Add true labels to the visualization
    true_labels = y_test.cpu().numpy()
    true_label_names = [class_names[label] for label in true_labels]

    # Visualize
    shap.image_plot(shap_values, X_test_uint8, labels=class_names, true_labels=true_label_names)

def plot_model_performance(model, class_names=None, model_details=None):
    import matplotlib.patches as patches

    color_palette = {
        'train': '#4B0082',
        'val': '#FF6347',
        'precision': '#F72585',
        'recall': '#4361EE',
        'f1': '#3A0CA3'
    }
    sns.set_theme(style="whitegrid")
    fig, axs = plt.subplots(2, 2, figsize=(16, 12))
    fig.suptitle('Model Performance Metrics', fontsize=16, fontweight='bold', color='#333333')
    fig.patch.set_facecolor('white')

    axs[0, 0].plot(model.history['train_loss'], label='Train Loss', color=color_palette['train'], linewidth=2, marker=".")
    axs[0, 0].plot(model.history['val_loss'], label='Validation Loss', color=color_palette['val'], linewidth=2, marker=".")
    axs[0, 0].set_title('Loss', fontweight='bold')
    axs[0, 0].set_xlabel('Epochs', color='#555555')
    axs[0, 0].set_ylabel('Loss', color='#555555')
    axs[0, 0].legend()

    axs[0, 1].plot(model.history['train_acc'], label='Train Accuracy', color=color_palette['train'], linewidth=2, marker=".")
    axs[0, 1].plot(model.history['val_acc'], label='Validation Accuracy', color=color_palette['val'], linewidth=2, marker=".")
    axs[0, 1].set_title('Accuracy', fontweight='bold')
    axs[0, 1].set_xlabel('Epochs', color='#555555')
    axs[0, 1].set_ylabel('Accuracy', color='#555555')
    axs[0, 1].legend()

    val_metrics = [
        ('val_precision', 'Validation Precision', color_palette['precision']),
        ('val_recall', 'Validation Recall', color_palette['recall']),
        ('val_f1_score', 'Validation F1 Score', color_palette['f1'])
    ]

    # Check if 'confusion_matrix' key exists and has values before plotting
    if 'confusion_matrix' in model.history and model.history['confusion_matrix']:

        for metric, label, color in val_metrics:
            axs[1, 0].plot(model.history[metric], label=label, color=color, linewidth=2, marker=".")

        axs[1, 0].set_title('Validation Metrics', fontweight='bold')
        axs[1, 0].set_xlabel('Epochs', color='#555555')
        axs[1, 0].set_ylabel('Score', color='#555555')
        axs[1, 0].legend()

        conf_matrix = np.array(model.history['confusion_matrix'][-1])

        # Generate class names if not provided
        if class_names is None:
            class_names = [f'Class {i}' for i in range(conf_matrix.shape[0])]

        sns.heatmap(conf_matrix,
                    cmap='RdPu',
                    # vmin=1.56,
                    # vmax=4.15,
                    square=True,
                    linewidth=0.3,
                    # cbar_kws={'shrink': .72},
                    annot_kws={'size': 12},
                    annot=True,
                    fmt='d',
                    ax=axs[1, 1],
                    xticklabels=class_names,
                    yticklabels=class_names
                    # cbar=False)
                    )

    axs[1, 1].set_title('Confusion Matrix', fontweight='bold')
    axs[1, 1].set_xlabel('Predicted Labels', color='#555555')
    axs[1, 1].set_ylabel('True Labels', color='#555555')

    for ax in axs.flat:
        ax.grid(True, linestyle='--', linewidth=0.5, color='lightgray', alpha=0.7)
        ax.spines['top'].set_visible(False)
        ax.spines['right'].set_visible(False)
        ax.spines['left'].set_linewidth(0.5)
        ax.spines['bottom'].set_linewidth(0.5)
        ax.tick_params(width=0.5)
        ax.grid(True, linestyle='--', linewidth=0.5, color='lightgray', alpha=0.7)
        ax.tick_params(colors='#555555')

        # Add model details at the top
    if model_details:
        detail_text = (
            r"$\bf{Model\ Name:}$" + f" {model_details.get('model_name', 'N/A')}\n"
            r"$\bf{Loss\ Function:}$" + f" {model_details.get('loss_function', 'N/A')}\n"
            r"$\bf{Optimizer:}$" + f" {model_details.get('optimizer', 'N/A')}\n"
            r"$\bf{Accuracy\ Metric:}$" + f" {model_details.get('accuracy_metric', 'N/A')}\n"
            r"$\bf{Learning\ Rate:}$" + f" {model_details.get('learning_rate', 'N/A')}\n"
            r"$\bf{Epochs:}$" + f" {model_details.get('epochs', 'N/A')}"
        )
        # fig.text(0.5, 0.96, detail_text, ha='center', va='center', fontsize=12, bbox=dict(facecolor='lightgray', edgecolor='black', boxstyle='round'))
        fig.text(
            0.02, 0.98, detail_text,
            ha='left', va='top', fontsize=8,
            color='#333333',
            # color='#555555',
            bbox=dict(facecolor='white', edgecolor='lightgray', boxstyle='round,pad=0.2', alpha=0.9),
            usetex=False
        )
    plt.tight_layout(rect=[0, 0, 1, 0.95])

    #plt.tight_layout()
    plt.show()

    # return fig

Explanation of what happens next: We will start by constructing NN models and then improve them to get optimal classification for the data.

Simple Neural Network

First, we would like to see what levels of accuracy we can achieve using a simple neural network, before moving on to more advanced models (models that are better suited to a dataset of images and trained models).

Model Architecture and Implementation
This model includes functionality to track performance metrics, enabling evaluation of its suitability for the dataset, memory constraints, and runtime efficiency in the context of assessing running performance.

Model Architecture (SimpleNN)

• The model takes three parameters: input_dimension (flattened input size, e.g., 224×224×3 for RGB images), hidden_layer_units (number of neurons in the hidden layer), and output_dimension (number of output classes).
• The architecture is defined using nn.Sequential, comprising:
  • nn.Flatten(): Converts input tensors (e.g., 2D or 3D images) into 1D vectors.
  • nn.Linear(input_dimension, hidden_layer_units): A fully connected layer mapping the flattened input to the hidden layer.
  • nn.ReLU(): Applies ReLU activation for non-linearity.
  • nn.Linear(hidden_layer_units, output_dimension): A fully connected layer mapping the hidden layer to the output classes.
  • nn.ReLU(): Applies ReLU activation to the output (note: this is unconventional for classification, as outputs typically use softmax or no activation for logits).
  • A history dictionary is initialized to store training and evaluation metrics, including loss, accuracy, precision, recall, F1-score, and confusion matrices for training, validation, and test sets.
• Forward Pass (forward):
  • Processes input tensors through the defined architecture, returning the output.
• Metric Tracking:
  • record_metric(metric_name, value): Appends a metric value to the corresponding list in history.
  • get_history(metric_name): Retrieves the list of values for a specified metric.
  • get_all_metrics(): Returns the entire history dictionary.

Purpose and Evaluation:

• Functionality: SimpleNN is a lightweight, fully connected neural network suitable for small-scale image classification tasks. Its simplicity minimizes memory and computational demands.
• Metric Tracking: The history dictionary enables comprehensive monitoring of model performance across training, validation, and test phases, facilitating analysis of metrics like accuracy, precision, recall, and F1-score.

Considerations:
The model's simplicity may limit its capacity to capture complex patterns in high-dimensional image data, potentially necessitating experimentation with deeper or convolutional architectures for improved performance.

This implementation provides a baseline model for initial testing and comparison in the project's evaluation phase.

class SimpleNN(nn.Module):
    def __init__(self, input_dimension: int, hidden_layer_units: int, output_dimension: int):
        super().__init__()

        self.model_architecture = nn.Sequential(
            nn.Flatten(),
            nn.Linear(input_dimension, hidden_layer_units),
            nn.ReLU(),
            nn.Linear(hidden_layer_units, output_dimension),
            nn.ReLU(),
        )

        self.history = {
            "train_loss": [],
            "train_acc": [],
            "train_precision": [],
            "val_loss": [],
            "val_acc": [],
            "val_precision": [],
            "val_recall": [],
            "val_f1_score": [],
            "test_loss": [],
            "test_acc": [],
            "test_precision": [],
            "test_recall": [],
            "test_f1_score": [],
            "confusion_matrix": []
        }

    def forward(self, input_tensor: torch.Tensor):
        return self.model_architecture(input_tensor)

    def record_metric(self, metric_name: str, value: float):
        if metric_name not in self.history:
            self.history[metric_name] = []
        self.history[metric_name].append(value)

    def get_history(self, metric_name: str):
        return self.history.get(metric_name, [])

    def get_all_metrics(self):
        return self.history

Model Configuration and Optimization Setup

To effectively train and evaluate the performance of the model model, we need to define both how the model learns and how its performance is assessed. This involves selecting appropriate components, namely a loss function, an optimizer, and relevant evaluation metrics.

Accuracy Metric
To evaluate the model's performance, we will utilize the torchmetrics.Accuracy metric. This metric calculates the proportion of correctly predicted samples to the total number of predictions.

Loss Function
We will use nn.CrossEntropyLoss(), which is one of the recommended loss functions for image classification.

Optimizer
We will utilize the Stochastic Gradient Descent (SGD) optimizer.

# input_dimension = 360 * 363 * 3
input_dimension = 224 * 224 * 3

device = 'cuda' if torch.cuda.is_available() else 'cpu'

model_nn = SimpleNN(
    input_dimension=input_dimension,
    hidden_layer_units=10,
    output_dimension=8,
).to(device)

# Hyperparameters
epochs = 40
num_labels = 8
learning_rate = 0.0001

loss_function = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(params=model_1.parameters(), lr=learning_rate)
accuracy_metric = Accuracy(
    task="multiclass", num_classes=num_labels, average="macro"
).to(device)

Run The Model

train_model(epochs, train_loader, val_loader, model_nn, loss_function, optimizer, accuracy_metric, device, num_classes=num_labels,
            early_stopping_patience=10, early_stopping_metric='val_loss', early_stopping_min_delta=0.0001, debug=False)

test_model(test_loader, model_1, loss_function, accuracy_metric, device, num_classes=num_labels)

Early stopping enabled: Monitoring 'val_loss', Patience=10, Mode='min', Min Delta=0.0001

Overall Progress: Epochs:   0%|           | Batch 0/40

Epoch 1: Training Phase:   0%|           | Batch 0/374

Epoch 1: Validation Phase:   0%|           | Batch 0/81

Epoch 2: Training Phase:   0%|           | Batch 0/374

Epoch 2: Validation Phase:   0%|           | Batch 0/81

Epoch 2: No improvement in val_loss for 1 epoch(s).

Epoch 3: Training Phase:   0%|           | Batch 0/374

Epoch 3: Validation Phase:   0%|           | Batch 0/81

Epoch 3: No improvement in val_loss for 2 epoch(s).

Epoch 4: Training Phase:   0%|           | Batch 0/374

Epoch 4: Validation Phase:   0%|           | Batch 0/81

Epoch 4: No improvement in val_loss for 3 epoch(s).

Epoch 5: Training Phase:   0%|           | Batch 0/374

Epoch 5: Validation Phase:   0%|           | Batch 0/81

Epoch 5: No improvement in val_loss for 4 epoch(s).

Epoch 6: Training Phase:   0%|           | Batch 0/374

Epoch 6: Validation Phase:   0%|           | Batch 0/81

Epoch 6: No improvement in val_loss for 5 epoch(s).

Epoch 7: Training Phase:   0%|           | Batch 0/374

Epoch 7: Validation Phase:   0%|           | Batch 0/81

Epoch 7: No improvement in val_loss for 6 epoch(s).

Epoch 8: Training Phase:   0%|           | Batch 0/374

Epoch 8: Validation Phase:   0%|           | Batch 0/81

Epoch 8: No improvement in val_loss for 7 epoch(s).

Epoch 9: Training Phase:   0%|           | Batch 0/374

Epoch 9: Validation Phase:   0%|           | Batch 0/81

Epoch 9: No improvement in val_loss for 8 epoch(s).

Epoch 10: Training Phase:   0%|           | Batch 0/374

Epoch 10: Validation Phase:   0%|           | Batch 0/81

Epoch 10: No improvement in val_loss for 9 epoch(s).

Epoch 11: Training Phase:   0%|           | Batch 0/374

Epoch 11: Validation Phase:   0%|           | Batch 0/81

Epoch 11: No improvement in val_loss for 10 epoch(s).

Early stopping triggered after 11 epochs.
Monitored metric 'val_loss' did not improve for 10 epochs.
Loading best model weights found during training.
Finished training loop.

[Test] Loss: 2.0432 | Accuracy: 0.13 | Precision: 0.04 | Recall: 0.19 | F1-Score: 0.07
Finished test evaluation.

Result Analysis

class_names = train_loader.dataset.classes

model_details = {
    "model_name": model_nn.__class__.__name__,
    "learning_rate": learning_rate.__str__(),
    "loss_function": loss_function.__class__.__name__,
    "optimizer": optimizer.__class__.__name__,
    "accuracy_metric": accuracy_metric.__class__.__name__,
    "epochs": epochs.__str__(),
}

plot_model_performance(model_nn, class_names, model_details=model_details)

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-103-ab81b69ab0c6> in <cell line: 0>()
     10 }
     11 
---> 12 plot_model_performance(model_nn, class_names, model_details=model_details)

<ipython-input-87-93bf990ef5b3> in plot_model_performance(model, class_names, model_details)
    181     axs[1, 0].legend()
    182 
--> 183     conf_matrix = np.array(model.history['confusion_matrix'][-1])
    184 
    185     # Generate class names if not provided

IndexError: list index out of range

Model Performance Review:
The image classification model, as evaluated over 7 epochs, demonstrates critically low performance and is currently unsuitable for the classification task.

• Accuracy: The validation accuracy is extremely low, indicating the model correctly classifies only a small fraction of images.
• Class Discrimination: The confusion matrix reveals a major flaw: the model almost exclusively predicts only one label class, failing to learn the distinguishing features of other cell types. This is corroborated by the very low validation recall and F1-score.
• Learning: While the loss curves show a decreasing trend (indicating the optimization process is running), this has not translated into effective classification learning for most classes.

In conclusion, the model fails to generalize and discriminate between the classes.

visualize_shap_values(
    model=model_nn,
    test_loader=test_loader,
    background_size=25,
    test_size=5
)

/usr/local/lib/python3.11/dist-packages/shap/explainers/_deep/deep_pytorch.py:255: UserWarning:

unrecognized nn.Module: Flatten

SHAP Plot Analysis
This plot aims to explain which parts of the input images are most influential in the model's prediction for specific examples.

In most examples, the positive SHAP values (red) are concentrated on the central blood cell, particularly highlighting the nucleus and sometimes specific cytoplasmic features. The surrounding background, including other out-of-focus cells, generally shows SHAP values close to zero or slightly negative (blue). This suggests that the model has learned to focus its attention on the primary cell within the image to make its classification decision, largely ignoring the background, which is a positive sign in terms of localization.

While the previous metrics indicated low overall classification accuracy, this SHAP analysis shows that the model is generally looking at the relevant regions (the cells themselves) to make predictions.

Conclusion:
The model seems to correctly identify and prioritize the features within the target cell. However, combined with the poor performance metrics seen earlier, this suggests that while the model knows where to look, it struggles to effectively interpret the features it sees to accurately differentiate between the various cell classes, especially the ones it frequently confuses according to the confusion matrix. The features extracted from these relevant regions are not sufficiently discriminative given the current model state.

We will try to improve our model so that it can also effectively interpret the features it sees.

Lightweight CNN

Model Architecture
The LightCNN is a convolutional neural network (CNN), designed for image classification. It comprises three sequential convolutional blocks followed by a multilayer perceptron (MLP) classifier. Each convolutional block employs a progressive channel expansion strategy, with the number of filters increasing from hidden_units to hidden_units*4 across the blocks. Each block consists of two convolutional layers with 3x3 kernels, batch normalization, ReLU activation, max-pooling (2x2), and dropout (0.2-0.3) to mitigate overfitting. The final block outputs hidden_units*4 channels, which are processed by an adaptive average pooling layer to produce a fixed-size feature map (1x1 per channel). The classifier then flattens this output and applies a two-layer MLP with 512 hidden units, ReLU activation, dropout (0.5), and a final linear layer mapping to the output classes. The model also includes functionality to record and retrieve training and evaluation metrics, such as loss, accuracy, precision, recall, and F1-score, stored in a history dictionary for performance tracking.

class LightCNN(nn.Module):
    def __init__(self, input_shape: int, hidden_units: int, output_shape: int):
        super().__init__()

        # Store the number of channels output by the last conv block
        self.final_conv_channels = hidden_units * 4

        # Progressive channel increase
        self.block_1 = nn.Sequential(
            nn.Conv2d(input_shape, hidden_units, 3, padding=1),
            nn.BatchNorm2d(hidden_units),
            nn.ReLU(),
            nn.Conv2d(hidden_units, hidden_units, 3, padding=1),
            nn.BatchNorm2d(hidden_units),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Dropout(0.2)
        )

        self.block_2 = nn.Sequential(
            nn.Conv2d(hidden_units, hidden_units*2, 3, padding=1),
            nn.BatchNorm2d(hidden_units*2),
            nn.ReLU(),
            nn.Conv2d(hidden_units*2, hidden_units*2, 3, padding=1),
            nn.BatchNorm2d(hidden_units*2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Dropout(0.2)
        )

        # Additional block for deeper feature extraction
        self.block_3 = nn.Sequential(
            nn.Conv2d(hidden_units*2, hidden_units*4, 3, padding=1),
            nn.BatchNorm2d(hidden_units*4),
            nn.ReLU(),
            nn.Conv2d(hidden_units*4, hidden_units*4, 3, padding=1),
            nn.BatchNorm2d(hidden_units*4),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Dropout(0.3)
        )

        # Improved classifier with MLP
        self.classifier = nn.Sequential(
            # Use Adaptive Average Pooling to get a fixed-size output (1x1) per channel
            # Output shape: (batch_size, final_conv_channels, 1, 1)
            nn.AdaptiveAvgPool2d((1, 1)),

            # Flatten the output for the linear layer
            # Output shape: (batch_size, final_conv_channels * 1 * 1)
            nn.Flatten(),

            # The input features to the Linear layer now only depend on the number of channels
            nn.Linear(self.final_conv_channels, 512),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(512, output_shape)
        )

        self.history = {
            "train_loss": [],
            "train_acc": [],
            "train_precision": [],
            "val_loss": [],
            "val_acc": [],
            "val_precision": [],
            "val_recall": [],
            "val_f1_score": [],
            "test_loss": [],
            "test_acc": [],
            "test_precision": [],
            "test_recall": [],
            "test_f1_score": [],
            "confusion_matrix": []
        }

    def forward(self, x: torch.Tensor):
        x = self.block_1(x)
        x = self.block_2(x)
        x = self.block_3(x)
        x = self.classifier(x)
        return x

    def record_metric(self, metric_name: str, value: float):
        """
        Records a metric value into the history.

        Parameters:
        - metric_name (str): The name of the metric (e.g., "loss", "accuracy").
        - value (float): The value of the metric to record.
        """
        if metric_name not in self.history:
            self.history[metric_name] = []
        self.history[metric_name].append(value)

    def get_history(self, metric_name: str):
        """
        Retrieves the history of a specific metric.

        Parameters:
        - metric_name (str): The name of the metric to retrieve.

        Returns:
        - List[float]: A list of recorded values for the specified metric.
        """
        return self.history.get(metric_name, [])

    def get_all_metrics(self):
        """
        Retrieves all recorded metrics in the model's history.

        Returns:
        - Dict[str, List[float]]: A dictionary containing all recorded metrics and their values.
        """
        return self.history

Model Configuration and Optimization Setup

torch.manual_seed(42)

light_cnn = LightCNN(
    input_shape=3,
    hidden_units=10,
    output_shape=len(class_names)
    ).to(device)

light_cnn

LightCNN(
  (block_1): Sequential(
    (0): Conv2d(3, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
    (3): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (4): BatchNorm2d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU()
    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (7): Dropout(p=0.2, inplace=False)
  )
  (block_2): Sequential(
    (0): Conv2d(10, 20, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(20, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
    (3): Conv2d(20, 20, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (4): BatchNorm2d(20, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU()
    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (7): Dropout(p=0.2, inplace=False)
  )
  (block_3): Sequential(
    (0): Conv2d(20, 40, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
    (3): Conv2d(40, 40, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (4): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU()
    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (7): Dropout(p=0.3, inplace=False)
  )
  (classifier): Sequential(
    (0): AdaptiveAvgPool2d(output_size=(1, 1))
    (1): Flatten(start_dim=1, end_dim=-1)
    (2): Linear(in_features=40, out_features=512, bias=True)
    (3): ReLU()
    (4): Dropout(p=0.5, inplace=False)
    (5): Linear(in_features=512, out_features=8, bias=True)
  )
)

class_names = train_loader.dataset.classes

# Hyperparameters
epochs = 40
num_labels = 8
learning_rate = 0.0001

# Initialize loss function, optimizer, and accuracy metric
loss_function = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(params=light_cnn.parameters(), lr=learning_rate)
accuracy_metric = Accuracy(task="multiclass", num_classes=num_labels).to(device)

train_model(epochs, train_loader, val_loader, light_cnn, loss_function, optimizer, accuracy_metric, device, num_classes=num_labels,
            early_stopping_patience=5, early_stopping_metric='val_loss', early_stopping_min_delta=0.0001, debug=False)

test_model(test_loader, light_cnn, loss_function, accuracy_metric, device, num_classes=num_labels)

[Test] Loss: 1.8757 | Accuracy: 0.35 | Precision: 0.24 | Recall: 0.35 | F1-Score: 0.25
Finished test evaluation.

Result Analysis

model_details = {
    "model_name": light_cnn.__class__.__name__,
    "learning_rate": learning_rate.__str__(),
    "loss_function": loss_function.__class__.__name__,
    "optimizer": optimizer.__class__.__name__,
    "accuracy_metric": accuracy_metric.__class__.__name__,
    "epochs": epochs.__str__(),
}

plot_model_performance(light_cnn, class_names, model_details=model_details)

Analysis of Performance Metrics:

Loss Curves:
Both the training loss and validation loss are generally decreasing over the epochs, which indicates the model is learning. The training loss decreases more steeply and reaches a lower final value than the validation loss. There is a consistent gap between the two curves, with the training loss being lower.

The model is successfully learning patterns from the training data. However, the gap between training and validation loss suggests some degree of overfitting. The model performs better on the data it has seen during training than on new, unseen data (validation set). While the validation loss is still decreasing (which is good), the overfitting might limit its generalization performance.

Accuracy Curves:
Both training and validation accuracy increase over epochs. There's a noticeable jump in validation accuracy starting around epoch 20, and then slightly decreasing or plateauing. The training accuracy consistently increases and is higher than the validation accuracy after the initial epochs.

The model's ability to correctly classify cells improves significantly up to about epoch 25-28 on the validation set. The plateauing/slight decrease afterwards suggests that further training with the current setup might not yield much better validation results and could even worsen overfitting.

The gap between training and validation accuracy confirms the overfitting noted in the loss plot. The sharp rise might indicate when the model started learning more discriminative features.

Validation Metrics (Precision, Recall, F1 Score):
These metrics mirror the validation accuracy trend, rising sharply around epoch 20. Precision seems slightly higher than Recall in the later epochs. The model's performance on the validation set stabilizes after epoch 25-30 across different metrics.

The overall balanced performance (F1 Score) on unseen data peaks at around 43%. This provides a more nuanced view than accuracy alone, confirming the model's modest predictive power on the validation set.

Confusion Matrix:
This provides crucial class-specific details: The overall low accuracy is clearly driven by the model's inability to distinguish several specific cell types. Certain classes dominate the misclassifications. Platelets seem visually distinct for the model, while others share features that confuse it.

The model is not uniformly effective across all 8 classes. It excels at some but fails significantly on others. The specific confusion patterns highlight which cell types are hardest for the current model to differentiate. This could be due to visual similarity, insufficient training examples for those classes, or the model architecture not being complex enough to capture subtle differences.

Overall Conclusions & Insights:
Learning Achieved but Performance Modest. The model is learning, but its ability to generalize to new data is limited. The gap between training and validation performance indicates overfitting. The model has learned the training data too well, potentially memorizing noise, which hinders its performance on the validation set.

The confusion matrix reveals the core problem: the model struggles dramatically with specific classes. Performance is highly uneven. This strongly suggests either:

• A significant imbalance in the number of training examples per class (the poorly performing classes might be underrepresented).
• High visual similarity between certain classes that the current model cannot resolve (e.g., Ig vs. Eosinophil/Neutrophil, Monocyte vs. Neutrophil).

Potential for Improvement:
While the current performance is modest, the analysis points towards clear areas for improvement: addressing overfitting, improving performance on specific weak classes (possibly via data augmentation, tackling class imbalance, or architectural changes), and analyzing the specific visual features causing confusion.

In my opinion, the model did not have enough epochs to learn the data. Running the model is expensive in terms of time and computational power, so we will try to improve the model and come back here later to run the model again.

Training vs. Validation Gap:
An unusual observation is that validation loss is consistently lower than training loss, and validation accuracy is higher than training accuracy throughout the epochs shown. This typically suggests potential issues such as problems with the dataset split (e.g., validation set being easier or not representative), significant regularization effects, or potential data leakage (though less likely given the low absolute performance).

Class Imbalance/Difficulty:
Like in the previous model, the model almost exclusively predicts only one label class, failing to learn the distinguishing features of other cell types.

Stability vs. Convergence:
While the validation metrics are stable, the low absolute values and limited training duration suggest the model may not have fully converged or that the current architecture/hyperparameters are insufficient.

visualize_shap_values(
    model=light_cnn,
    test_loader=test_loader,
    background_size=25,
    test_size=5
)

/usr/local/lib/python3.11/dist-packages/shap/explainers/_deep/deep_pytorch.py:255: UserWarning:

unrecognized nn.Module: Flatten

Across most input images and corresponding SHAP maps, the model primarily focuses its attention on the main blood cell in the center, largely ignoring the background red blood cells or empty space. This is excellent, as it shows the model is concentrating on the object of interest.

• For cells with prominent nuclei (Rows 1, 4, 5), the SHAP values are heavily concentrated on the nucleus. This indicates the model relies heavily on nuclear shape, size, and texture to differentiate classes.
• For the cell with distinct granules (Row 2), the SHAP map in the second column shows strong positive influence from the granulated cytoplasm and parts of the nucleus. This aligns with the expectation that granules are key identifying features for Eosinophils and likely explains why this class was relatively well-classified in our previous results.
• For the Platelet-like cells (Row 3), the positive SHAP values (e.g., columns 8 & 9) are tightly focused on the small cell bodies/dots, confirming the model correctly identifies these tiny structures as the most important feature for predicting the Platelet class (which also had high accuracy).

Explaining Predictions (and Misclassifications)

For a single input image (e.g., Row 1), looking across the columns shows how different features influence the prediction for different potential classes. The nucleus might strongly support prediction towards class 'X' (red/pink in column 4) but argue against predicting class 'Y' (blue in column 2).

Potential Source of Confusion:
The heavy reliance on the nucleus for multiple cell types could explain the confusion between them seen in the confusion matrix. If the model doesn't perfectly capture the subtle differences in nuclear segmentation or chromatin pattern required to distinguish these types, it might produce positive SHAP values for the wrong class based on general nuclear presence. For example, the features making the model consider class 8 for the Neutrophil in Row 4 (strong red on nucleus) might be similar enough to features in Monocytes or Lymphocytes to cause misclassification sometimes.

Well-Defined Features:
Classes identified by very distinct features (like the small size of Platelets or the specific granules of Eosinophils) seem to generate more localized and decisive positive SHAP values for their correct class column, correlating with their better classification accuracy.

Conclusions Linking SHAP to Previous Results:

• Model is Learning Meaningful Biology: The SHAP analysis confirms that the CNN is generally focusing on biologically relevant structures (nucleus, cytoplasm, granules) to make its decisions, rather than spurious background correlations.
• Visual Confirmation of Performance Differences: The clear, focused SHAP highlights for well-classified cells like Platelets and Eosinophils contrast with potentially more ambiguous or overlapping patterns for cells that are often confused, like different types of mononuclear or polymorphonuclear cells relying heavily on nuclear morphology.

Reinforces Overfitting/Generalization Issues:
While the model looks at the right areas, the SHAP patterns don't guarantee it's using the optimal or most robust features within those areas to generalize perfectly, aligning with the overfitting noted earlier. It might be slightly overfitting to specific nuclear textures or shapes in the training set.

Conclusion:
If trying to improve differentiation between confused classes, analyzing SHAP maps for misclassified examples could reveal which specific features are misleading the model. This could guide targeted data augmentation or architectural adjustments.

The plot provides valuable visual evidence that our model is learning pertinent features but also helps visualize why differentiating between certain visually similar cell types is challenging for the current model, corroborating the findings from the performance metrics and confusion matrix.

Improve CNN Model

This model incorporates architectural enhancements specifically designed to improve the model's ability to interpret complex visual features, addressing the previous limitation where the model could locate relevant areas (as shown by SHAP) but failed to classify them accurately.

The primary improvement is a substantial increase in the model's representational capacity within the convolutional blocks, scaling the number of filters dramatically from (10, 20, 40) in the previous model to (64, 128, 256) in CNNModel. This increased depth allows CNNModel to learn a richer hierarchy of more intricate and subtle features, which is critical for distinguishing between visually similar cell types that the lower-capacity ModelV3 struggled with. Additionally, this refines the classifier by applying Global Average Pooling (GAP) directly to the final rich feature maps (256 channels) before the linear layers. This GAP layer encourages the convolutional features to be more directly discriminative for the classes and reduces spatial parameter complexity compared to the previouse models' approach, leading to potentially more robust and interpretable feature extraction directly relevant to the classification task, rather than just localization. Combined with adjusted dropout for regularization, these changes provide the model the necessary tools to not only find the relevant cell features but also to understand and interpret them more effectively for accurate classification.

Model Architectures

This model represents a standard CNN architecture built sequentially.

• Structure: It consists of three main convolutional blocks (block_1, block_2, block_3).
• Convolutional Blocks: Each block contains:
  • Two Conv2d layers with 3x3 kernels and padding to preserve spatial dimensions within the block.
  • BatchNorm2d layers after each convolution for normalization and improved training stability.
  • ReLU activation functions to introduce non-linearity.
  • A MaxPool2d layer at the end of each block to downsample the feature maps, reducing spatial dimensions and increasing the receptive field.
  • Dropout layers with increasing probability (0.2, 0.3, 0.4) after each block to mitigate overfitting.
• Filter Progression: The number of filters increases progressively through the blocks (hidden_units, hidden_units * 2, hidden_units * 4), allowing the network to learn increasingly complex features.
• Classification Head:
  • A AdaptiveAvgPool2d(1) (Global Average Pooling - GAP) layer drastically reduces the spatial dimensions of the final feature maps to 1x1 while retaining channel information. This reduces the number of parameters compared to flattening large feature maps directly.
  • A classifier sequence follows, consisting of:
    • Flatten layer.
    • A Linear layer reducing dimensions to 256.
    • ReLU activation.
    • A strong Dropout (0.5).
    • The final Linear layer mapping to the output_classes (8 blood cell types).
• Training: This model is trained "from scratch," meaning its weights are randomly initialized and learned solely based on the provided blood cell dataset.

Design Considerations:

• Incremental Channel Expansion: Feature channels double with each block (from hidden_units to hidden_units*4), allowing the network to learn increasingly complex features.
• Regularization Strategy: Progressive dropout rates (0.2 -> 0.3 -> 0.4 -> 0.5) help prevent overfitting while allowing deeper layers to learn more robust features.
• Batch Normalization: Applied after each convolutional layer to stabilize and accelerate training.
• Global Average Pooling: Reduces parameters compared to fully connected layers, improving generalization.

class CNNModel(nn.Module):
    def __init__(self, input_channels: int, hidden_units: int, output_classes: int):
        super().__init__()

        # Convolutional feature extractor
        self.block_1 = nn.Sequential(
            nn.Conv2d(input_channels, hidden_units, kernel_size=3, padding=1),
            nn.BatchNorm2d(hidden_units),
            nn.ReLU(),
            nn.Conv2d(hidden_units, hidden_units, kernel_size=3, padding=1),
            nn.BatchNorm2d(hidden_units),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Dropout(0.2)
        )

        self.history = {
            "train_loss": [],
            "train_acc": [],
            "train_precision": [],
            "val_loss": [],
            "val_acc": [],
            "val_precision": [],
            "val_recall": [],
            "val_f1_score": [],
            "test_loss": [],
            "test_acc": [],
            "test_precision": [],
            "test_recall": [],
            "test_f1_score": [],
            "confusion_matrix": []
        }


        self.block_2 = nn.Sequential(
            nn.Conv2d(hidden_units, hidden_units * 2, kernel_size=3, padding=1),
            nn.BatchNorm2d(hidden_units * 2),
            nn.ReLU(),
            nn.Conv2d(hidden_units * 2, hidden_units * 2, kernel_size=3, padding=1),
            nn.BatchNorm2d(hidden_units * 2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Dropout(0.3)
        )

        self.block_3 = nn.Sequential(
            nn.Conv2d(hidden_units * 2, hidden_units * 4, kernel_size=3, padding=1),
            nn.BatchNorm2d(hidden_units * 4),
            nn.ReLU(),
            nn.Conv2d(hidden_units * 4, hidden_units * 4, kernel_size=3, padding=1),
            nn.BatchNorm2d(hidden_units * 4),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Dropout(0.4)
        )

        # Global Average Pooling and Classifier
        self.gap = nn.AdaptiveAvgPool2d(1)  # GAP layer
        self.classifier = nn.Sequential(
            nn.Flatten(),
            nn.Linear(hidden_units * 4, 256),  # Reduced FC size
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(256, output_classes)
        )

    def forward(self, x: torch.Tensor):
        x = self.block_1(x)
        x = self.block_2(x)
        x = self.block_3(x)
        x = self.gap(x)
        x = self.classifier(x)
        return x

Model Configuration and Optimization Setup

# Model parameters
input_channels = 3  # RGB channels
hidden_units = 64
output_classes = len(train_dataset.classes)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class_names = train_loader.dataset.classes

# Instantiate the model
model_cnn = CNNModel(input_channels=input_channels,
                     hidden_units=hidden_units,
                     output_classes=output_classes
                     ).to(device)

model_cnn

CNNModel(
  (block_1): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU()
    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (7): Dropout(p=0.2, inplace=False)
  )
  (block_2): Sequential(
    (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
    (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU()
    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (7): Dropout(p=0.3, inplace=False)
  )
  (block_3): Sequential(
    (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
    (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU()
    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (7): Dropout(p=0.4, inplace=False)
  )
  (gap): AdaptiveAvgPool2d(output_size=1)
  (classifier): Sequential(
    (0): Flatten(start_dim=1, end_dim=-1)
    (1): Linear(in_features=256, out_features=256, bias=True)
    (2): ReLU()
    (3): Dropout(p=0.5, inplace=False)
    (4): Linear(in_features=256, out_features=8, bias=True)
  )
)

# Hyperparameters
epochs = 40
num_labels = 8
learning_rate = 0.001

# Loss function, optimizer, and accuracy metric
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(model_cnn.parameters(), lr=1e-4)
accuracy_metric = Accuracy(task="multiclass", num_classes=num_labels).to(device)

Run The Model

hist = train_model(epochs, train_loader, val_loader, model_cnn, loss_function, optimizer, accuracy_metric, device, num_classes=num_labels,
            early_stopping_patience=10, early_stopping_metric='val_loss', early_stopping_min_delta=0.0001, debug=False)

Early stopping enabled: Monitoring 'val_loss', Patience=10, Mode='min', Min Delta=0.0001

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Epoch 33: No improvement in val_loss for 7 epoch(s).

Epoch 34: Training Phase:   0%|           | Batch 0/374

Epoch 34: Validation Phase:   0%|           | Batch 0/81

Epoch 34: No improvement in val_loss for 8 epoch(s).

Epoch 35: Training Phase:   0%|           | Batch 0/374

Epoch 35: Validation Phase:   0%|           | Batch 0/81

Epoch 35: No improvement in val_loss for 9 epoch(s).

Epoch 36: Training Phase:   0%|           | Batch 0/374

Epoch 36: Validation Phase:   0%|           | Batch 0/81

Epoch 37: Training Phase:   0%|           | Batch 0/374

Epoch 37: Validation Phase:   0%|           | Batch 0/81

Epoch 37: No improvement in val_loss for 1 epoch(s).

Epoch 38: Training Phase:   0%|           | Batch 0/374

Epoch 38: Validation Phase:   0%|           | Batch 0/81

Epoch 38: No improvement in val_loss for 2 epoch(s).

Epoch 39: Training Phase:   0%|           | Batch 0/374

Epoch 39: Validation Phase:   0%|           | Batch 0/81

Epoch 39: No improvement in val_loss for 3 epoch(s).

Epoch 40: Training Phase:   0%|           | Batch 0/374

Epoch 40: Validation Phase:   0%|           | Batch 0/81

Epoch 40: No improvement in val_loss for 4 epoch(s).
Finished training loop.

test_model(test_loader, model_cnn, loss_function, accuracy_metric, device, num_classes=num_labels)

[Test] Loss: 0.0680 | Accuracy: 0.98 | Precision: 0.98 | Recall: 0.98 | F1-Score: 0.98
Finished test evaluation.

Result Analysis

model_details = {
    "model_name": model_cnn.__class__.__name__,
    "learning_rate": learning_rate.__str__(),
    "loss_function": loss_function.__class__.__name__,
    "optimizer": optimizer.__class__.__name__,
    "accuracy_metric": accuracy_metric.__class__.__name__,
    "epochs": epochs.__str__(),
}

plot_model_performance(model_cnn, class_names, model_details=model_details)

Analysis of Performance Metrics:

Loss Curves:
There's a very sharp drop in both training and validation loss within the first epochs, quickly settling at a very low value. Crucially, the training loss and validation loss curves track each other very closely throughout the epochs, with minimal gap between them. There's a bit more noise/spikiness in the validation loss later on, but it remains low.

This indicates extremely efficient learning and, most importantly, significantly reduced overfitting compared to the previous model. The model generalizes exceptionally well, performing almost identically on training and unseen validation data. The improvements have effectively addressed the overfitting problem seen in the first model. The model converges quickly to a good solution.

Accuracy Curves:
Accuracy (both training and validation) jumps dramatically to within the first epochs. Both curves then plateau and remain very close together, hovering above 95% for the rest of the training. This represents a massive leap in performance compared to the validation accuracy of the previous model. The close tracking confirms the excellent generalization. The improved model achieves very high classification accuracy, demonstrating its effectiveness.

Validation Metrics (Precision, Recall, F1 Score):
Similar to accuracy, these validation metrics shoot up rapidly, stabilizing around 95% within the first epochs. Precision, Recall, and F1-Score track each other closely. This confirms the high performance isn't just high accuracy, but also a good balance between precision (minimizing false positives) and recall (minimizing false negatives) on the validation set. The model is robustly effective across different performance viewpoints on unseen data.

Confusion Matrix:
The confusion matrix shows a night-and-day difference compared to the previous model:

• The numbers along the diagonal are very high for all classes.
• Misclassifications are sparse and very small (mostly 0s, 1s, or 2s).

The model now successfully distinguishes between all 8 classes with high accuracy. The severe confusion issues and failures on classes like Basophil, Ig, Erythroblast, Lymphocyte, and Monocyte seen previously are almost entirely resolved.

The model improvements have allowed it to learn the distinguishing features of even the previously difficult-to-classify cell types. The classification is highly reliable across the board.

Overall Conclusions and Insights:

• Dramatic Performance Leap: The improved model is vastly superior to the initial version. Overall accuracy has jumped from aprox 43% to about 97%, which is a remarkable improvement.
• Overfitting Solved: The significant overfitting issue present in the first model has been effectively mitigated. The improved model generalizes extremely well.
• Class Discrimination Mastered: The model now reliably differentiates all 8 blood cell types, including those it completely failed on before. This indicates the improvements allowed the model to capture more complex and subtle features.
• Highly Effective Improvements: The architectural changes that we implemented were highly successful in addressing the key weaknesses of the original model.
• Near-Optimal Performance: With accuracy/F1 scores in the high 90s, this model performs exceptionally well on this dataset. The remaining minor errors represent the most challenging cases.

In conclusion, our improved CNN model is a resounding success based on these metrics. It's accurate, generalizes well, and reliably classifies all target blood cell types in your dataset.

visualize_shap_values(
    model=model_cnn,
    test_loader=test_loader,
    background_size=25,
    test_size=10
)

/usr/local/lib/python3.11/dist-packages/shap/explainers/_deep/deep_pytorch.py:255: UserWarning:

unrecognized nn.Module: Flatten

Similar to the previous model, the improved model correctly focuses its attention on the central blood cell in each image, largely ignoring the background. It primarily uses features related to the nucleus (shape, size, segmentation) and overall cell morphology (e.g., the small size and density of the platelet).

Increased Decisiveness and Confidence:

• Stronger Influence: Notice the SHAP value range on the color bar (-0.008 to +0.008) is wider than the previous plot (-0.0004 to +0.0004). This indicates that individual pixel regions now have a stronger influence (both positive and negative) on the predictions, suggesting a more confident model.
• Clearer Signals: For each input image, there typically appears to be one or perhaps two output classes (columns) where key features generate strong positive (red/pink) SHAP values, while generating strong negative (blue) values for many other classes.

Specific Examples:

• Neutrophil Example (Row 2): The segmented nuclear lobes produce a very strong positive signal for the class in column 8, while strongly pushing away from predictions for most other classes. This shows the model confidently using the defining feature.
• Platelet Example (Row 3): The platelet body generates an extremely strong positive signal for the class in column 9 and strong negative signals for others. This explanation is very localized and decisive.
• Lymphocyte/Monocyte Example (Row 1): The nucleus strongly supports predictions for classes in columns 2, 4, and 6, while strongly opposing predictions for columns 3, 5, 7, 8, and 9. This highlights how nuclear features are used to discriminate, likely resulting in a confident prediction for one of the positively influenced classes.

Visual Confirmation of High Accuracy:
The clarity and strength of these SHAP explanations align well with the aprox. 97% accuracy achieved by the improved model. The model isn't just getting the right answer; it seems to be doing so by strongly identifying key, relevant visual features and using them decisively.

Comparison to Previous SHAP Plot:
Compared to the SHAP plot for the original, less accurate model, these explanations appear:

• More Focused: The positive contributions often highlight the most critical identifying features more intensely.
• Less Ambiguous: There seem to be fewer instances where features provide weak or mixed signals for multiple classes simultaneously. The model appears more certain about which features support which classification.
• Stronger Opposition: The negative (blue) SHAP values also appear stronger, indicating the model is more effectively ruling out incorrect classes based on the observed features.

Conclusions:
The improved model confidently utilizes biologically relevant features (nuclear morphology, cell size, granularity patterns implied by nuclear focus) to make predictions. The decisiveness and clarity of the SHAP explanations visually confirm the high accuracy and robustness of the improved model. The model strongly associates key features with specific classes. The SHAP analysis reinforces that the improvements made to the model allowed it not only to be more accurate but also to build a more confident and interpretable relationship between visual features and class predictions. The model has clearly learned highly discriminative feature representations.

In essence, this SHAP plot provides strong visual evidence supporting the quantitative results, showing how your improved model achieves its high performance by focusing strongly and decisively on the key identifying characteristics of the different blood cell types.

ResNet18

This model utilizes a well-established architecture, ResNet18, leveraging transfer learning.

• ResNet Architecture: ResNet (Residual Network) models are known for their "skip connections" or "residual blocks." These connections allow gradients to flow more easily through deeper networks, mitigating the vanishing gradient problem and enabling the training of significantly deeper models compared to traditional sequential CNNs. ResNet18 is a specific variant with 18 layers containing learnable weights.
• Transfer Learning: Instead of training from scratch, we load ResNet18 with weights pre-trained on the large and diverse ImageNet dataset. The assumption is that the features learned on ImageNet (edges, textures, basic shapes) are useful starting points for understanding features in blood cell images.
• Fine-Tuning:
  • Feature Extractor: The convolutional base of the pre-trained ResNet18 (self.base) is used as a fixed or partially trainable feature extractor. We freeze the initial layers (for param in list(self.base.parameters())[:-15]: param.requires_grad = False), retaining their learned ImageNet features, while allowing the later layers to adapt slightly to the blood cell data.
  • Custom Classifier: The original ResNet18 classifier (usually designed for 1000 ImageNet classes) is removed (self.base.classifier = nn.Sequential(), self.base.fc = nn.Sequential()). It's replaced with a new, smaller classification head (self.block) specific to our task:
    • A Linear layer reducing the 512 features from the ResNet base to 128.
    • ReLU activation.
    • Dropout (0.2).
    • The final Linear layer mapping to our num_classes (8).
  • Differential Learning Rates: A key aspect of fine-tuning is often using different learning rates. Here, AdamW optimizer is configured with a smaller learning rate (3e-5) for the pre-trained base layers (allowing slow adaptation) and a larger learning rate (8e-4) for the newly added classifier block (self.block), enabling it to learn the task-specific mapping more quickly.

ResNet 18 Architecture

Original ResNet-18 Architecture

Original and Modified ResNet Architectures

---

Comparison to the previous convolutional network:

Feature	CNNModel	ModelResNet18 (ResNet18 Transfer Learning)
Architecture	Sequential custom design	Standard ResNet18 architecture with skip connections
Initialization	Random weights (trained from scratch)	Pre-trained weights from ImageNet
Training Strategy	Full training on blood cell data	Fine-tuning (partial freezing, custom head)
Depth	Relatively shallow (3 main blocks)	Deeper (18 layers + residual connections)
Gradient Flow	Standard backpropagation	Enhanced by skip connections
Feature Learning	Learns features solely from blood cell data	Leverages general features learned from ImageNet
Convergence	Potentially slower, requires more data/epochs	Often faster convergence due to pre-training
Optimization	Single optimizer, single learning rate likely	Differential learning rates for base vs. new layers
Flexibility	High flexibility in design	Constrained by the ResNet architecture (base)

class ModelResNet18(nn.Module):
    def __init__(self, num_classes=8):
        super().__init__()
        weights = ResNet18_Weights.DEFAULT
        self.base = models.resnet18(weights=weights)

        for param in list(self.base.parameters())[:-15]:
            param.requires_grad = False

        self.block = nn.Sequential(
            nn.Linear(512, 128),
            nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(128, num_classes),
        )
        self.base.classifier = nn.Sequential()
        self.base.fc = nn.Sequential()

    def get_optimizer(self):
        return torch.optim.AdamW([
            {'params': self.base.parameters(), 'lr': 3e-5},
            {'params': self.block.parameters(), 'lr': 8e-4}
        ])

    def forward(self, x):
        x = self.base(x)
        x = self.block(x)
        return x

class ResNetTrainer(nn.Module):
    def __init__(self, train_loader, val_loader, test_loader=None, num_classes=8, device='cpu'):
        super().__init__()
        self.train_loader = train_loader
        self.val_loader = val_loader
        self.test_loader = test_loader
        self.device = device

        self.model = ModelResNet18().to(self.device)
        self.optimizer = self.model.get_optimizer()
        self.loss_fxn = nn.CrossEntropyLoss()

        # Initialize all metrics
        self.accuracy = Accuracy(task="multiclass", num_classes=num_classes).to(self.device)
        self.precision = Precision(task="multiclass", num_classes=num_classes).to(self.device)
        self.recall = Recall(task="multiclass", num_classes=num_classes).to(self.device)
        self.f1 = F1Score(task="multiclass", num_classes=num_classes).to(self.device)
        self.confusion_matrix_metric = ConfusionMatrix(num_classes=num_classes, task="multiclass").to(self.device)

        self.history = {
            "train_loss": [],
            "train_acc": [],
            "train_precision": [],
            "val_loss": [],
            "val_acc": [],
            "val_precision": [],
            "val_recall": [],
            "val_f1_score": [],
            "test_loss": [],
            "test_acc": [],
            "test_precision": [],
            "test_recall": [],
            "test_f1_score": [],
            "confusion_matrix": []
        }

    def reset_metrics(self):
        """Reset all metrics"""
        self.accuracy.reset()
        self.precision.reset()
        self.recall.reset()
        self.f1.reset()
        self.confusion_matrix_metric.reset()

    def training_step(self, x, y):
        pred = self.model(x)
        loss = self.loss_fxn(pred, y)

        # Calculate all metrics
        acc = self.accuracy(pred, y)
        prec = self.precision(pred, y)
        rec = self.recall(pred, y)
        f1 = self.f1(pred, y)

        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

        return loss, acc, prec, rec, f1

    def validation_step(self, x, y):
        with torch.inference_mode():
            pred = self.model(x)
            loss = self.loss_fxn(pred, y)

            # Calculate all metrics
            acc = self.accuracy(pred, y)
            prec = self.precision(pred, y)
            rec = self.recall(pred, y)
            f1 = self.f1(pred, y)

        return loss, acc, prec, rec, f1

    def process_batch(self, loader, step):
        step_name = step.__name__.replace('_', ' ').capitalize() if hasattr(step, '__name__') else "Processing"

        loss, acc, prec, rec, f1 = 0, 0, 0, 0, 0
        self.reset_metrics()
        for X, y in tqdm(loader, total=len(loader), desc=f"Processing Batch - {step_name}",
                         leave=False, position=2, bar_format="{l_bar}{bar} | Batch {n_fmt}/{total_fmt}"):
            X, y = X.to(self.device), y.to(self.device)
            l, a, p, r, f = step(X, y)
            loss += l.item()
            acc += a.item()
            prec += p.item()
            rec += r.item()
            f1 += f.item()

        n = len(loader)
        return loss / n, acc / n, prec / n, rec / n, f1 / n

    def train(self, epochs, print_progress=False):
        for epoch in tqdm(range(epochs), desc="Overall Progress: Epochs", leave=True,
                          position=0, bar_format="{l_bar}{bar} | Batch {n_fmt}/{total_fmt}"):
            self.reset_metrics()
            # Training phase
            train_loss, train_acc, train_prec, train_rec, train_f1_score = self.process_batch(
                self.train_loader, self.training_step
            )

            # Validation phase
            val_loss, val_acc, val_prec, val_rec, val_f1_score = self.process_batch(
                self.val_loader, self.validation_step
            )

            # Update history
            metrics = [
                train_loss, val_loss,
                train_acc, val_acc,
                train_prec, val_prec,
                train_rec, val_rec,
                train_f1_score, val_f1_score
            ]

            for item, value in zip(self.history.keys(), metrics):
                self.history[item].append(value)

            if print_progress == True:
                print(
                    f"[Epoch: {epoch + 1}] "
                    f"Train: [loss: {train_loss:.3f} acc: {train_acc:.3f} "
                    f"prec: {train_prec:.3f} rec: {train_rec:.3f} f1: {train_f1_score:.3f}] "
                    f"Val: [loss: {val_loss:.3f} acc: {val_acc:.3f} "
                    f"prec: {val_prec:.3f} rec: {val_rec:.3f} f1: {val_f1_score:.3f}]"
                )
            if print_progress == True:
                print(f"\nEpoch {epoch + 1}/{epochs} Performance Report:")
                print(f"└─ [Train] Loss: {train_loss:.4f} | Accuracy: {train_acc * 100:.2f}% | Precision: {train_prec:.2f}")
                print(f"└─ [Validation] Loss: {val_loss:.4f} | Accuracy: {val_acc * 100:.2f}% | Precision: {val_prec:.2f} | Recall: {val_rec:.2f} | F1-Score: {val_f1_score:.2f}")
        print("Finished training and validation.")

    def test(self, print_result=True):
        """
        Evaluate the model on the test set after training is complete.
        Returns a dictionary with test metrics.
        """
        if self.test_loader is None:
            raise ValueError("Test loader was not provided during initialization")

        self.model.eval()
        self.reset_metrics()

        test_loss = 0
        with torch.inference_mode():
            for X, y in tqdm(self.test_loader, desc=f"Testing Phase",
                             leave=False, position=2, bar_format="{l_bar}{bar} | {n_fmt}/{total_fmt}"):
                X, y = X.to(self.device), y.to(self.device)
                pred = self.model(X)
                loss = self.loss_fxn(pred, y)
                test_loss += loss.item()

                # Calculate all metrics
                self.accuracy(pred, y)
                self.precision(pred, y)
                self.recall(pred, y)
                self.f1(pred, y)
                self.confusion_matrix_metric(pred, y)

        # Calculate average test loss
        test_loss /= len(self.test_loader)

        # Compute final metrics
        test_acc = self.accuracy.compute()
        test_precision = self.precision.compute()
        test_recall = self.recall.compute()
        test_f1 = self.f1.compute()
        confusion_matrix = self.confusion_matrix_metric.compute()

        self.history["test_loss"].append(test_loss)
        self.history["test_acc"].append(test_acc.item())
        self.history["test_precision"].append(test_precision.item())
        self.history["test_recall"].append(test_recall.item())
        self.history["test_f1_score"].append(test_f1.item())
        self.history["confusion_matrix"].append(confusion_matrix.cpu().numpy())
        self.confusion_matrix_metric.reset()
        if print_result == True:
            print(f"\n[Test] Loss: {test_loss:.4f} | Accuracy: {test_acc * 100:.2f} | Precision: {test_precision:.2f} | Recall: {test_recall:.2f} | F1-Score: {test_f1:.2f}")
            print("Finished test evaluation.")

Model Configuration and Optimization Setup

device = 'cuda' if torch.cuda.is_available() else 'cpu'
epochs = 40

model_resnet = ResNetTrainer(train_loader, val_loader, test_loader, num_classes=8, device=device)

Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
100%|██████████| 44.7M/44.7M [00:00<00:00, 219MB/s]

Run The Model

model_resnet.train(epochs=epochs)

model_resnet.test()

Testing Phase:   0%|           | 0/81

[Test] Loss: 0.2016 | Accuracy: 96.61 | Precision: 0.97 | Recall: 0.97 | F1-Score: 0.97
Finished test evaluation.

Result Analysis

model_details = {
    "model_name": "ResNet18",
    "learning_rate": "3e-5 - 8e-4",
    "loss_function": "CrossEntropyLoss",
    "optimizer": "AdamW",
    "accuracy_metric": "MulticlassAccuracy",
    "epochs":epochs,
}

plot_model_performance(model_resnet, class_names, model_details=model_details)

The training loss plummets to nearly zero within the first 5 epochs and stays there. The validation loss also drops rapidly but stabilizes at a slightly higher, yet still very low, level. There's a minimal, stable gap between the two curves. This demonstrates extremely fast convergence and effective learning, characteristic of powerful architectures like ResNet. The very small gap indicates excellent generalization with minimal overfitting.

ResNet18 learns the task efficiently and generalizes very well to the validation data.

Accuracy Curves:
Training accuracy jumps to nearly 100% almost immediately. Validation accuracy also rises extremely quickly, reaching and maintaining a level around 99%. The gap between training and validation accuracy is tiny. This represents a further improvement over the previous CNN model (which reached to about 97%). The ResNet18 architecture achieves near-perfect accuracy on the training set and maintains exceptionally high accuracy on unseen data.

The ResNet18 model achieves state-of-the-art accuracy for this task within the scope of the previous models.

Validation Metrics:
All three metrics on the validation set rapidly reach approximately 99% within the first 10 epochs and remain consistently high. This confirms the outstanding performance observed in the accuracy plot. The model exhibits both high precision (very few false positives) and high recall (very few false negatives) across the classes.

• Conclusion: The model provides highly balanced and extremely effective classification performance on the validation set.

Confusion Matrix (Bottom Right):
The confusion matrix is almost perfectly diagonal. Very high numbers for all 8 classes. Extremely sparse and involve very few samples. The most notable, though still very small, errors involve Basophils/Ig and Neutrophils/Ig confusion. ResNet18 demonstrates superior discrimination between all cell types compared to the previous models. Even the minor confusions seen in the "improved CNN" results are further reduced.

• Conclusion: The model distinguishes between the 8 classes with exceptional reliability.

Conclusions and Insights:
ResNet18 delivers the best performance among the models presented, achieving approximately 99% accuracy and F1-score on the validation set.

• Architecture Suitability: The model architecture is highly effective for this image classification problem, likely leveraging its deep structure and residual connections and potentially pre-training, to capture complex features effectively.
• Excellent Generalization and Efficiency: The model converges very quickly to a solution that generalizes extremely well, showing minimal overfitting despite its high capacity.
• Near-Perfect Class Separation: It achieves outstanding separation between the blood cell classes, minimizing errors even for visually similar types.

In summary, the ResNet18 model provides a highly accurate, robust, and efficient solution for our blood cell classification task, outperforming the custom CNN models previously evaluated. Its performance suggests it's capturing the intricate visual details needed for differentiation very effectively.

shap_partition(model=model_resnet.model, test_dataset=test_dataset, device=device, num_samples=8)

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Focus on Key Morphological Features:
The SHAP heatmaps consistently demonstrate that the ResNet18 model focuses its attention on the most pertinent morphological features of the blood cells, ignoring the background red blood cells or empty space. The shape, size, and internal patterns of the nucleus are clearly highlighted by SHAP values.

Clear Discrimination Between Classes:
The plot effectively visualizes how the model discriminates. Features strongly supporting the correct class (red/pink in the correct column) often simultaneously provide strong negative evidence (blue) against other, incorrect classes. This strong positive signal for the correct class and negative signal for incorrect classes explains the model's high accuracy and low confusion rates seen in the metrics.

Consistency and Interpretability:
The explanations are consistent across multiple examples of the same cell type. The features highlighted by positive SHAP values for the correct predictions align well with human expert knowledge used for cell identification (e.g., nuclear segmentation for neutrophils, small size for platelets). This increases trust in the model's decision-making process.

Visual Confirmation of High Performance:
The clarity, focus, and biological relevance of these SHAP explanations provide strong qualitative support for the quantitative of about 99% accuracy achieved by ResNet18. The model isn't just accurate but it appears to be accurate for the right reasons, focusing on scientifically meaningful features.

Conclusions:
The model has successfully learned to identify and utilize the key visual features that differentiate the blood cell classes, focusing on biologically relevant aspects like nuclear morphology and cell size/shape. The SHAP explanations visualize the model's strong discriminative power, showing how specific features strongly support the correct classification while actively arguing against incorrect ones. This reflects a confident and accurate model. The fact that the model relies on understandable and relevant features (as shown by SHAP) increases confidence in its predictions and its potential for real-world application. The model's SHAP explanations appear particularly clear, focused, and aligned with the expected identifying features, mirroring its superior performance metrics.

In conclusion, the SHAP analysis for the ResNet18 model powerfully complements its outstanding performance metrics, demonstrating that it makes highly accurate predictions based on relevant and interpretable visual evidence within the cell images.

Vision Transformer (ViT)

While CNNs are traditionally strong performers in image classification, ViTs offer an alternative approach inspired by the success of transformers in natural language processing. They treat an image as a sequence of patches and use self-attention mechanisms to capture global dependencies, which can be beneficial for complex image recognition tasks.

"ViT models have significant advantages over traditionally used deep learning architectures. Firstly, ViT models offer a more general and universal architecture. To process visual data, these models first decompose the image into small patches and then process these patches with a set of attention mechanisms. This approach allows the model to learn more general features and detect objects at different scales. The application of transformer-based approaches for classifying medical images is an emerging field of research."
— Katar, O.; Yildirim, O. An Explainable Vision Transformer Model Based White Blood Cells Classification and Localization.

Vision Transformers represent a significant shift from traditional CNNs for image recognition tasks. Inspired by the success of Transformer models in Natural Language Processing, ViTs apply the Transformer architecture directly to images.

"Unlike traditional CNNs that use spatial convolutions to extract features from images, Vision Transformer (ViT) models that use self-attentional mechanisms to capture the relationships between different regions of an image can improve performance."
— Bhojanapalli, Srinadh, et al. "Understanding robustness of transformers for image classification."

This highlights a key advantage of ViT architectures: their ability to model long-range dependencies more effectively than convolution-based approaches.

The core idea is to treat an image not as a grid of pixels, but as a sequence of smaller, fixed-size patches (like words in a sentence). These patches are flattened, linearly embedded, and position embeddings are added to retain spatial information. This sequence of vectors is then fed into a standard Transformer encoder, which uses self-attention mechanisms.

The self-attention mechanism allows the model to weigh the importance of different patches relative to each other, enabling it to capture long-range dependencies and global context within the image, unlike CNNs which primarily focus on local features through convolutional filters. Finally, a classification head is typically added to the output of the Transformer encoder to perform the image classification task.

CNN & ViT Architectures for Leukocyte Classification

Convolutional Neural Network (CNN)

Vision Transformer (ViT)

The left diagram illustrates a typical Convolutional Neural Network approach. An input image (blood cell) passes through sequential layers of convolution and pooling (feature extractor) that progressively learn hierarchical features. Finally, these features are flattened and fed into a classifier, often a Multi-Layer Perceptron (MLP Head), to output the probability for each blood cell class. The right diagram shows the Vision Transformer (ViT) approach. The input image is divided into fixed-size patches. These patches are flattened, linearly projected into embeddings, and combined with position embeddings to retain spatial information. This sequence is fed into a Transformer Encoder, which uses self-attention to model relationships between patches. An MLP Head then classifies the image based on the Transformer's output. The diagram also shows how techniques like Class Activation Mapping (CAM) can be used with ViT for localization.

ViT Components

Hierarchical View of the Multi-Head Self-Attention Mechanism in ViT

ViT Pipeline for Image Classification

The left diagram provides a detailed view of the Multi-Head Self-Attention mechanism, a core component of the Transformer Encoder. It shows how input embeddings are linearly projected into Query (Q), Key (K), and Value (V) vectors for multiple "heads". Attention scores are calculated using scaled dot-product between Q and K, applied to V, and the results from different heads are concatenated and linearly projected to produce the final output. This allows the model to jointly attend to information from different representation subspaces.

The right diagram gives another perspective on the overall ViT architecture. It highlights the process of patching the image, linear projection, adding position embeddings, and including an extra learnable class embedding used for classification. The sequence is processed by the Transformer Encoder (shown as stacked blocks). A detailed view of a single Transformer Encoder block is provided on the right, showing the sequence of operations: Layer Normalization (Norm), Multi-Head Attention, residual connection (+), another Norm layer, an MLP block, and a final residual connection. The output corresponding to the [class] embedding is passed to the final MLP Head for classification.

For more details, see this (exelent) article.

ViTClassifier - Model Architecture

The ViTClassifier class uses a pre-trained Vision Transformer model (vit_b_16) provided by torchvision.models. Using a pre-trained model allows us to benefit from knowledge learned on a large dataset (transfer learning). This often leads to better performance and faster convergence compared to training a model from scratch, especially with smaller datasets.

• weights = ViT_B_16_Weights.DEFAULT - This line selects the best available pre-trained weights for the vit_b_16 model.

• self.vit = models.vit_b_16(weights=weights) - An instance of the ViT model (base variant 'b', patch size 16x16) is loaded with the specified pre-trained weights. The bulk of the model's layers (patch embedding, transformer encoders) are retained.

Adapt Classifier Head:

• num_ftrs = self.vit.heads.head.in_features - We get the number of input features expected by the original classification layer (head) of the pre-trained ViT.

• self.vit.heads.head = nn.Linear(num_ftrs, num_classes) - The original classification head, which was trained for ImageNet's 1000 classes, is replaced with a new nn.Linear layer. This new layer takes the features extracted by the ViT backbone (num_ftrs) and outputs scores for our specific number of blood cell classes (num_classes). Only this new layer will have its weights initialized randomly; the rest of the network retains the pre-trained weights.

from torchvision.models import ViT_B_16_Weights

class ViTClassifier(nn.Module):
    def __init__(self, num_classes):
        super(ViTClassifier, self).__init__()
        # Load pre-trained ViT model
        weights = ViT_B_16_Weights.DEFAULT  # Best available weights
        self.vit = models.vit_b_16(weights=weights)

        # Replace the classifier head
        num_ftrs = self.vit.heads.head.in_features
        self.vit.heads.head = nn.Linear(num_ftrs, num_classes)

        self.history = {
                "train_loss": [],
                "train_acc": [],
                "train_precision": [],
                "val_loss": [],
                "val_acc": [],
                "val_precision": [],
                "val_recall": [],
                "val_f1_score": [],
                "test_loss": [],
                "test_acc": [],
                "test_precision": [],
                "test_recall": [],
                "test_f1_score": [],
                "confusion_matrix": []
            }

    def forward(self, x):
      return self.vit(x)

    def record_metric(self, metric_name: str, value: float):
        if metric_name not in self.history:
            self.history[metric_name] = []
        self.history[metric_name].append(value)

    def get_history(self, metric_name: str):
        return self.history.get(metric_name, [])

    def get_all_metrics(self):
        return self.history

Model Configuration and Optimization Setup

# Create the model
vit_model = ViTClassifier(num_classes=8)

input_dimension = 224 * 224 * 3

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

vit_model.to(device)

# Hyperparameters
epochs = 40
num_labels = 8
learning_rate = 0.001

loss_function = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(params=vit_model.parameters(), lr=learning_rate)
accuracy_metric = Accuracy(task="multiclass", num_classes=num_labels, average="macro").to(device)

Run The Model

hist = train_model(epochs, train_loader, val_loader, vit_model, loss_function,
                   optimizer, accuracy_metric, device, num_classes=num_labels, debug=False)

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test_model(test_loader, vit_model, loss_function, accuracy_metric, device, num_classes=num_labels)

[Test] Loss: 0.0796 | Accuracy: 0.98 | Precision: 0.98 | Recall: 0.98 | F1-Score: 0.98
Finished test evaluation.

Result Analysis

model_details = {
    "model_name": "Vision Transformer (ViT)",
    "learning_rate": learning_rate.__str__(),
    "loss_function": loss_function.__class__.__name__,
    "optimizer": optimizer.__class__.__name__,
    "accuracy_metric": accuracy_metric.__class__.__name__,
    "epochs": epochs.__str__(),
}

plot_model_performance(vit_model, class_names, model_details=model_details)

Analysis of ViT Performance Metrics:

Loss Curves:
Both training and validation losses drop very sharply within the first epochs and then plateau at very low levels. There is a very small, consistent gap between the two curves. The ViT model learns extremely quickly and efficiently converges to a low-loss state. The minimal gap indicates excellent generalization and negligible overfitting, similar to the ResNet18 model.

In conclusion, the ViT architecture is highly effective at learning the underlying patterns in the data and generalizes well to unseen examples.

Accuracy Curves:
Training accuracy rapidly approaches 99%. Validation accuracy also climbs very quickly, and then stabilizing around 97-98%. The validation accuracy remains consistently high after the initial climb. This places the ViT model's performance significantly above our LightCNN model and its improved CNN version, and very close to the ResNet18 model. In conclusion, ViT achieves top-tier accuracy for this classification task, demonstrating its power in image recognition.

Validation Metrics:
These metrics follow the accuracy trend, quickly rising to high levels. Precision stabilizes slightly higher than Recall and F1-score. This confirms the high performance is well-balanced. An F1-score of about 98% indicates robustness in terms of both minimizing false positives and false negatives. In conclusion, the model demonstrates strong and balanced predictive capability on the validation set.

Confusion Matrix:
The matrix is predominantly diagonal, indicating high accuracy across all classes. High counts along the diagonal for all labels. Errors are sparse and involve small numbers. Minor confusions persist. The ViT model successfully distinguishes between the vast majority of cells. The remaining minor errors highlight the most challenging distinctions, similar to those seen with ResNet18, although the number of specific errors might be slightly different. In conclusion, the model provides excellent class separation, comparable in quality to the ResNet18 model, with minimal confusion.

Overall Conclusions and Insights:
The Vision Transformer model achieves excellent results, with very high validation accuracy and F1-score, placing it among the best-performing models tested for this task, alongside ResNet18. This demonstrates that transformer-based architectures, originally developed for natural language processing, are highly effective for medical image classification tasks like this one, rivaling state-of-the-art CNNs like ResNet.

In addition, the model learns rapidly and generalizes very well, indicating its ability to capture relevant features without significant overfitting, even with a potentially higher learning rate. It successfully differentiates between the 8 complex blood cell types with high reliability, as evidenced by the clean confusion matrix.

In summary, the Vision Transformer model is a powerful and effective choice for this blood cell classification problem, delivering performance nearly identical to the ResNet18 model. Both architectures represent the state-of-the-art for this dataset.

shap_partition(model=vit_model, test_dataset=test_dataset, device=device, num_samples=10)

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We can visualize how the model separates classes. Features highlighted in red/pink for the correct class column often show up as blue (negative influence) in columns corresponding to incorrect classes, visually confirming the model's discriminative ability.

Similar to the previous models, the ViT model consistently focuses its attention on the morphologically relevant parts of the cells. It identifies features within the main cell body, particularly the nucleus, and the specific shape of objects like platelets, while ignoring the background.

Clear and Localized Explanations:
The SHAP heatmaps provide clear visual explanations. The positive SHAP values are sharply focused on the segmented nuclear lobes, which are the defining characteristic. The positive values are tightly localized to the object itself and are concentrated on the nucleus and cell body.

The explanations remain consistent for different images of the same cell type, indicating the model has learned stable representations. For example, the way it highlights neutrophil nuclei is similar across all neutrophil examples shown.

These SHAP explanations are remarkably similar in clarity, focus, and feature highlighting to those generated for the ResNet18 model. Both architectures, despite their fundamental differences (CNN vs. Transformer), appear to have learned to exploit similar key visual features (nuclear shape, cell size, etc.) in a focused manner to achieve high accuracy.

Conclusions:
The ViT model's high performance is based on identifying and utilizing biologically relevant and interpretable features, as clearly shown by SHAP. The model correctly assigns high importance to the defining characteristics of each cell type (e.g., nuclear lobes, object size/shape) when predicting that class. The clear, consistent, and focused nature of these SHAP explanations provides strong qualitative evidence that supports the high quantitative accuracy achieved by the model. It gets the right answers by looking at the right things.

The high similarity between the ViT and ResNet18 SHAP plots suggests that both highly optimized architectures converged on using similar, effective visual strategies for this specific classification task.

In conclusion, the SHAP analysis for the ViT model reinforces its status as a top-performing model for this task. It achieves high accuracy by effectively learning and focusing on the key distinguishing visual features of the different blood cell types in a way that is both interpretable and comparable in quality to the ResNet18 explanations.

Summary

This project aimed to develop and evaluate deep learning models for the automated classification of eight distinct types of blood cells from provided image data. The process involved an iterative approach, starting with a baseline model and progressively improving performance and exploring different architectures. We began with the objective of classifying eight types of blood cells from a given image dataset. The goal was to explore the data thoroughly and identify effective classification methods, particularly comparing traditional and deep learning approaches.

We first organized the dataset and we then proceeded to validate the data's integrity. We checked for corrupted paths and analyzed image dimensions because inconsistencies can hinder analysis and model training. We identified that most images were 360x363 pixels but found some outliers with different dimensions. We investigated these dimensional outliers further, comparing their visual quality (sharpness, hue, etc.) to the standard images. While minor differences were noted (outliers slightly less sharp), they represented a small dataset fraction (< 3%). We decided to retain these images initially to maximize data usage, concluding the quality difference was likely negligible for robust models.

To understand inherent data properties, we analyzed the distribution of the labels. This revealed significant class imbalance, which immediately indicated the need for stratified data splitting and the use of metrics for evaluation. We also analyzed color channel brightness, confirming a reddish hue typical of staining and identifying brightness outliers, which upon inspection appeared to be valid extremes (e.g., very dark or sparse images) rather than errors, justifying their retention.

We quantitatively evaluated image noise levels per color channel because excessive noise can impede learning. The analysis showed consistently low noise across the dataset, leading us to conclude that specific denoising preprocessing was unnecessary.

Feature Engineering Strategy:
To represent images numerically for classification, we explored two paths: extracting classical HOG features and extracting deep features using a pre-trained Vision Transformer (ViT). This comparison was crucial to determine if modern deep features offered advantages over traditional methods for this specific task.

We employed PCA and t-SNE to visualize the HOG and ViT feature spaces. This was done to assess the inherent separability of the classes based on the extracted features. The visualizations compellingly showed that ViT features produced well-defined, separable clusters for each cell type, whereas HOG features resulted in heavily overlapping classes, confirming the superior representational power of the deep features.

Data Preparation for Modeling:
We prepared the dataset for model training by applying different transformations. We split the data into training, validation, and test sets (70/15/15) using stratification to ensure the class imbalance was mirrored in all subsets. PyTorch DataLoaders were then set up for efficient batch processing.

Baseline Modeling (ViT Features + ML):
To leverage the high-quality ViT features, we first tested simpler classifiers. Logistic Regression achieved excellent results, indicating the features were largely linearly separable. However, a Decision Tree performed poorly due to overfitting, suggesting its structure wasn't optimal for this feature space without ensembling or significant pruning.

Deep Learning:
We then attempted end-to-end training, starting with a basic fully connected network. This model failed to learn effectively, highlighting its inadequacy for complex image data compared to specialized architectures.

We then build CNNs. The first model showed learning capability but suffered from significant overfitting and yielded only modest performance. Based on this, we improved our model with increased depth and better regularization (Global Average Pooling), which dramatically improved accuracy and resolved the overfitting issue, demonstrating the importance of architectural choices.

To achieve potentially higher performance, we employed transfer learning. Fine-tuning a pre-trained ResNet18 model delivered outstanding results, benefiting from weights learned on a large dataset. Similarly, fine-tuning a pre-trained Vision Transformer (ViT) also produced excellent, comparable results, confirming the efficacy of both state-of-the-art CNN and Transformer architectures.

To ensure the models weren't just accurate by chance, we applied SHAP analysis. This interpretability technique confirmed that the Improved CNNs, ResNet18, and ViT models were focusing on biologically relevant features (like nuclear morphology, cell size) to make their classifications, increasing our confidence in their validity.

Overall Conclusion: The sequential process demonstrated that while initial data exploration revealed challenges like class imbalance, deep learning provided effective solutions. Deep features vastly outperformed classical ones. Transfer learning with robust architectures like ResNet18 and ViT proved superior to training simpler models or custom CNNs from scratch, achieving near-perfect classification accuracy by effectively learning discriminative visual patterns.

Personal Experience

Difficulties
This project presented several significant challenges. Firstly, delving into both the specific domain of hematology and the intricate details of various deep learning algorithms required navigating subjects where I had limited prior background. Secondly, the sheer variety of techniques explored, from classical HOG to multiple neural network architectures (CNNs, ResNet, ViT), demanded a broad understanding of different methodologies. Lastly, a persistent practical difficulty was the substantial requirement for memory and computational resources, particularly for training the larger deep learning models, which often constrained the experimentation process.

Surprises and Insights
I was particularly struck by a couple of outcomes. Initially, I anticipated that the models might struggle significantly due to the high visual similarity between some blood cell types and the potential distraction from background cells. However, it was truly insightful to see, especially through SHAP visualizations, how effectively the well-trained models learned to pinpoint discriminative features and focus solely on the target cell, overcoming these anticipated difficulties. Furthermore, I was genuinely surprised by the power of Vision Transformers (ViT) in this image classification task, performing on par with highly optimized CNNs like ResNet18. The underlying concept of adapting sequence-processing ideas from natural language processing (treating image patches contextually) was a fascinating and powerful approach that I found particularly compelling.

My Take
My primary takeaway is the reinforcement of several core principles in practical machine learning. Firstly, the indispensable value of a high-quality, well-understood dataset became evident. It feels to me as it's the foundation for any meaningful modeling effort.

Secondly, I recognized the importance of going beyond surface-level accuracy metrics and actively investigating how the model learns. Utilizing interpretability tools like SHAP to confirm that the models were focusing on relevant biological features, rather than potential artifacts, was crucial for building confidence in the outcomes, especially given the initial concerns about cell similarities and background elements.

Furthermore, this project underscored the significance of iterative development (trial and error process). Reaching the final high-performing models wasn't a linear path, it involved building upon initial attempts, diagnosing issues like overfitting in earlier models, and progressively refining the architecture or approach until satisfactory results were achieved. It's also worth noting that in this iterative process, I developed two additional models that also produced strong results. However, to maintain focus and clarity in the final report, I decided to exclude them, as the selected models already effectively illustrated the key findings and demonstrated the successful achievement of high classification performance. Ultimately, this experience highlights that success often lies in the combination of quality data, persistent experimentation, and a commitment to understanding the model's decision-making process.

References

1. Asghar, R., Kumar, S., & Hynds, P. (2024). Automatic classification of 10 blood cell subtypes using transfer learning via pre-trained convolutional neural networks. Informatics in Medicine Unlocked, 49, 101542.
2. Acevedo, A., Merino, A., Alférez, S., Molina, Á., Boldú, L., & Rodellar, J. (2020). A dataset of microscopic peripheral blood cell images for development of automatic recognition systems. Data in brief, 30, 105474.
3. pytorch.org/blog/tensor-memory-format-matters/#pytorch-best-practice
4. Katar, O.; Yildirim, O. An Explainable Vision Transformer Model Based White Blood Cells Classification and Localization. Diagnostics 2023, 13, 2459.
5. Bhojanapalli, Srinadh, et al. "Understanding robustness of transformers for image classification." Proceedings of the IEEE/CVF international conference on computer vision. 2021.
6. Patil, A. M., M. D. Patil, and G. K. Birajdar. "White blood cells image classification using deep learning with canonical correlation analysis." Irbm 42.5 (2021): 378-389.